I welcome you all to this lecture in the course Introduction to Paninian Grammar. We are studying the concepts of Karaka, the definitions given in the Paninian grammatical tradition of this very important concept of Karaka. We have studied these technical terms as defined in the South Niasutras in the Ashtadhyay. Overall the Karaka terms can be also represented in this particular format. In this lecture we shall study this way of explaining the definitions of Karaka which were provided in the Ashtadhyay which we studied in the previous lecture. These explanations are provided by scholars who lived in the post 18th century era. Mainly there are the three authors, Bhattavjidikshita who composed the text of Vaya Karana Matvon Madjana Karika, Konda Bhatt who composed the text of Vaya Karana Bhushana and Nagesha Bhatt who composed the text of Vaya Karana Siddhanta, Manjusha and Spota Vada and so on. These are the three main authors whose contribution is taken into consideration while explaining the definitions of Karaka which were studied in the previous lecture. The main point of view can be summed up as that of the Shabdabodha, the verbal cognition point of view. So let us study all these definitions one by one from this point of view. We need to understand the nature of Kriya as explained by these scholars, these theoreticians. So nature of Kriya is the following, Kriya is denoted by the verbal root. So to use the technical terminology we can say that Kriya is Vachya and verbal root or Dhatu is Vachakar. So there is this relationship of Vachavachakabhava between the Kriya and Dhatu. Kriya has two parts of its nature, of its nature, Phala and Vyapara. Vyapara is the process involved in an action and Phala is the result of that process. It is the Vyapara which generates the Phala. There is a Jani Janiakabhava relation between them. Phala gets generated by the Vyapara or the process and Vyapara generates the Phala. Here are some examples. If we take the verbal root gamma which means to go the Vyapara and the Phala with reference to the action of going can be presented in the following manner. Sanyoga janaka, pada prakshe pada vyapara. The process beginning with taking steps by food which generates the association. This is what is the meaning of gamma. Sanyoga is the Phala, pada prakshe pada is the vyapara. Sanyoga is generated by pada prakshe pada prakshe pada generates Sanyoga. Similarly, if we look at the verbal root tyaja which means to leave its meaning can be rendered in this particular terminology as Vibhaga janaka pada prakshe pada vyapara. The process beginning with taking steps by food which generates the separation. This is what is the meaning of tyaja. Vibhaga is the Phala and pada prakshe pada is the vyapara. As far as a human being is concerned, then we look at the verbal root pachya which means to cook. So, viklutty janaka adhishrayanadi avashrayana paryanta vyapara. This is what is the meaning of pachya. The process beginning with the placing of the vessel on fire and ending in removing the vessel from the fire. This is the process and this process generates the softening of grains. This is what is the meaning of action of cooking denoted by the verbal root pachya. So, viklutty is the phala and adhishrayanadi avashrayana. This is all that is called vyapara. This is how the meaning, the nature of kriya can be explained. So, to put it diagrammatically, dhatu denotes kriya. Kriya has two inherent aspects, vyapara and phala. So, if we take the example of gama, gamana going is the meaning and pada prakshepadi is the vyapara and saiyoga is the phala. So, now let us try to define all the karakas that we studied in the previous lecture in this particular way. Apadana was defined by 1424 as drvam apaye apadanam. Now, it is defined as prakritadhatu avachya vibhagashraya buddhi. The cognition of the substratum of the separation which is not denoted by the relevant verbal root is termed as apadana. I repeat, the cognition of the substratum of the separation which is not denoted by the relevant verbal root is termed apadana. Buddhi is cognition, substratum is ashraya, of the separation is vibhag, which is not denoted avachya by the relevant verbal root is prakritadhatu and this is what is called apadana. So, let us take the example of gama, meaning to go, which means saiyoga janaka pada prakshaipadi vyapara, which means the process beginning with taking steps by food which generates the association. But this saiyoga is generated only after the separation takes place, but the separation is not the meaning of the verbal root this must be remembered. So, the separation here is not the meaning of the verbal root, so it is prakritadhatu avachya. So, there is vibhag, but this vibhag is not the meaning avachya of the prakritadhatu, relevant dhatu that is used here gama. So, now the substratum of such a vibhag is termed as apadana over here. So, if we look at the same example, he goes to kashi from prayaga. So, the speaker here describes the action of going, which is saiyoga janaka vyapara and this saiyoga is not possible without a vibhag and this vibhag is nothing but the apaya mentioned in the sutra 1424 dravam apaye apadanam. This vibhag is not denoted by a verbal root shown with dotted arrow in the diagram that we shall see in the next slide. So, the substratum of such a vibhag is prayaga, which is what is a dhruva in the sutra dravam apaye apadanam. And therefore, now following the definition of apadana prakritadhatu avachya vibhagashraya, this prayaga is termed an apadana. So, here is a diagrammatical representation. Here is a dhatu which denotes kriya and dhatu denotes kriya and kriya has got two components, vyapara and kala. Now, this kriya can have vibhag but that is not the meaning of the dhatu and that is why this dotted arrow, this arrow which is cut, this arrow indicates that this is not the meaning. Now, the ashraya of such a vibhag is called apadana. The concrete example given over here is the verbal root gamma which means gamana, this is a kriya, this is a dhatu. Gamana kriya consists of say yoga as its phala and pada prakshepadi as its vyapara. Now, this say yoga is not possible unless the vibhag takes place. But this vibhag is not the meaning of gamana. It is invariably present as far as the meanings of the gamana are concerned. It is entailed in this meaning but it is not directly denoted by the verbal root gamma. Now, such a vibhag and its ashraya now in this case it is prayaga, this is what is termed as apadana in this case. This is how the term apadana gets explained from the point of view of shabdabodha and that is also shown to have correlation with the wordings in the sutras in the ashradhyayi defining apadana. Let us now study the definition of sampradana given in these texts and also the modern tradition. Prakritadhatu vacha phala uddesya buddhi is what is sampradana. The cognition of the recipient of the result of the action denoted by the relevant verbal root is termed sampradana. And I repeat, the cognition of the recipient of the result of the action denoted by the relevant verbal root is termed sampradana. The cognition that is a buddhi of the recipient uddesya of the result phala of the action that is denoted by the relevant verbal root prakritadhatu vacha phala uddesya buddhi is what is termed as sampradana. So, an element thought to be the purpose of generating the result of the action denoted by the verbal root is termed sampradana. This is what is karmana yam abhiprati. Let us take an example. The same example, sa krishi palaya gam dadati. She gives a cow to a farmer. That is the meaning of this sentence. Here the speaker describes the action of giving, cow is the object, she is the agent. It is thought that through this object namely the cow, the agent that is she wants to reach krishi vala the farmer. So, krishi vala the farmer is the uddesya recipient of the object of this action and hence krishi vala the farmer is termed a sampradana. This is how the definition of sampradana has given in this scheme also matches with the definition given in the sutra of ashradhyay. So, here is a diagrammatical representation. Here is a dhatu which denotes kriya and vyapara and phala are its parts. The ashray of phala is called karma and this karma is related to an entity which has the relation of uddesya. This is called sampradana. So, if we take the instance of pachar, the action denoted by the verbal root pachai is cooking pachana in this case. It has got a vyapara and viklithi is the result of this action. Odana is the substatum of this viklithi and the uddesya of this odana is yadnyadatta. So, yadnyadatta is termed sampradana in this case. Let us now look at the next karaka and its definition provided in this particular scheme, karana. Karana is defined as the cognition of the element which generates the result of the process immediately after it gets operationalized is termed karana. I repeat, the cognition of the element which generates the result of the process immediately after it gets operationalized is termed karana. The cognition buddhi of the element which generates the result of the process immediately after it gets operationalized is termed as karana. So, an element which is thought as being operational and immediately producing the result of the action is termed as karana. This is what is sadhaka tamatva. Let us look at the example. This is the same example. A boy writes the homework by a pen. Here the speaker describes the action of writing. A boy is the agent and the homework the object. Now the pen sva is thought to be such that when operationalized nishthapyapara the result of the action of writing is produced. And therefore pen likhani is termed a karana because when it gets operationalized the homework which is the result of the process of writing that comes into being. If the pen does not get operationalized it does not bring about the result of the action of writing namely this homework. That is the reason why likhani or the pen is termed as karana. This is what is sadhaka tamatva. This is what karana, this is how karana gets defined and this is how sadhaka tamatva gets linked to the to this definition. Diagrammatically we can show it in this way. Now let us go to adhikarana. Adhikarana is defined as So the cognition of the substratum of the process as well as result of the action through the agent and the object is adhikarana. The cognition of the substratum of the process as well as result of the action through the agent and the object is what is adhikarana. The cognition, buddhi of the substratum adharatva of the process vyapara as well as result phala of the action through the adhikarana the agent karthru and the object karma and an element thought to be the substratum of the karthru or the karma and through them thought to be the substratum of the process and the result respectively is called adhikarana. So adhikarana is indirectly related to the process and the result. Let us take the example. A girl sits on the mat. The speaker here wants to describe the action of sitting, a girl being the agent. The mat is thought to be the substratum or adhara of this girl who is playing the role of kartha and that is happening through this girl of the action of sitting. So the mat which is the substratum of the kartha and kartha is the substratum of vyapara. So the mat is linked to the action of sitting through the kartha that is this girl and hence mat is termed adhikarana also termed as karthradhikarana the adhikarana of the kartha. Similarly in the second example devadatta sthalyam odhanam pacchati devadatta cooks the rice in a vessel it is the speaker the speaker describes the action of cooking devadatta is thought to be the agent odhana is thought to be the object sthaly is thought to be the substratum or adhara of this odhana which is karma and through this odhana sthaly is the substratum of the result of the action and hence sthaly is termed an adhikarana precisely karma adhikarana. So this can be shown diagrammatically in this particular fashion adhatu denotes a kriya and kriya has these two parts vyapara and phala vyapara ashraya is called kartha phala ashraya is called karma and now the ashraya of this kartha is called adhikarana karthradhikarana and the ashraya of this karma is called karma adhikarana. So in a way adhikarana is defined as dhatu vachya vyapara ashraya ashraya so ashraya of the ashraya of vyapara denoted by the verbal root is the karthradhikarana and the ashraya of the ashraya of phala denoted by the verbal root is called karma adhikarana. Let us now study the definition of karma as given in this particular scheme. This is what is karma. The cognition of the substratum of the result of the action denoted by the relevant verbal root which is generated by the process denoted by the relevant verbal root is termed karma. I repeat the cognition of the substratum of the result of the action denoted by the relevant verbal root which is generated by the process denoted by the relevant verbal root is termed karma. The cognition, buddhi of the substratum of the result of the action phala ashrayatva denoted by the relevant verbal root dhatvartha prakrutadhatvartha which is generated janya by the process vyapara denoted by the relevant verbal root prakrutadhatvartha is termed karma. This is what is thought of as being most desired by the agent through the process. In other words, Ipsita tamatvar as described in 1449, kartu ripsita tamam karma. Let us look at the example, balika pathasalaam gacchandi, the female students go to school. Here the speaker describes the action of going through the verb gacchandi, balika being thought of as the agent. The substratum of saiyoga which is the result of the process of going as denoted by the same verbal root is thought to be the pathasala and hence pathasala is the halashraya which is generated by the process and hence it is Ipsita tamam and hence it is termed as karma following this particular definition. So, let us look at this from this diagram. Here we have dhatvartha denoting kriya or verbal action. This verbal action is made up of these two inherent components vyapara and phala, vyaparashraya is karta and phalashraya is termed as karma by this definition. So, the definition of karma is dhatvartha phalashraya to put it simply. But we also say that dhatvartha vyapara janya. So, the vyapara the process which is the meaning of dhatu which generates the phala, the substratum of such a phala is karma. dhatvartha vyapara janya dhatvartha phalaashraya is karma. To take an example pachya is the verbal root, pachana is the kriya, patraagni saiyoga etc. This seems to be the vyapara and viklutti seems to be the phala. Now the ashraya of this phala viklutti which is odana is termed as karma. And lastly karta as the karaka defined in 1454 as prakritadhatu vachya vyaparaashraya buddhi. The cognition of the substratum of the process denoted by the relevant verbal root is termed karta. The cognition of the substratum of the process denoted by the verbal root is termed karta. The cognition buddhi of the substratum ashraya of the process vyapara denoted vachya by the relevant verbal root prakritadhatu is termed as karta. An element in which is located the most important part of the action denoted by the verbal root namely the process is thought to be the karta. This is what is the independence or swatantratva which is mentioned in 1454 in the definition of karta namely swatantrakkarta. Let us take the example devadattah sthalyam odanam yadnyadatta yapachati devadatta cooks the rice in a vessel for yadnyadatta. In this case odana that is rice is thought to be the object sthaly is thought to be the substratum of odana yadnyadatta is thought to be the recipient of this karma. Now it is this devadatta who is thought to be the one who brings all these above together to accomplish the action of cooking. So devadatta is thought to be the substratum of the process of cooking in other words swatantra are independent and hence devadatta is termed as karta. To show this diagrammatically we can say that the verbal root which denotes an action has got two components vyapara and phala the vyapara ashraya is karta. In order to show karta you do not need to show any other karaka but that is not the case with other karaka. In order to show karma you have to show karta in order to show karana you have to show karma etc. This is what also shows the independence of karta. This is what also shows the swatantratva of karta. Swatantratva that makes a karta. So let us take the example of gamma the verbal root the verbal root gamma which means the action of going gamana it has got the vyapara namely padha prakshepa etc. and sanyoga being the result. Now the ashraya of this padha prakshepa is devadatta. If this padha prakshepa does not happen sanyoga cannot come into existence. So now devadatta is the substratum of this padha prakshepa therefore by definition prakritadhatu vatsya gamana vyapara padha prakshepadi ashraya and so devadatta is the karta. And this brings about the entire action accomplishment of the action. So devadatta is defined as karta in this particular case following the definition provided and studied earlier prakritadhatu vatsya vyapara ashraya karta. So to represent all the karakas in a diagram this will be that diagram. We have a prakritadhatu we have a kriya as its meaning vyapara and phala as its inherent components it is that phala which is produced by this karana it is the sampradana which is the udeshya of this karma it is the adhikarana which is the ashraya of karma it is another adhikarana which is the ashraya of karta it is karta which is the ashraya of vyapara it is karma which is the ashraya of phala and it is the vibhaga which is not denoted by the verbal root the ashraya of such a vibhaga is apadana. This is how all the karakas can be defined using this particular scheme. In this discussion we demonstrated and explained these definitions and also explained them diagrammatically and also showed the link of this explanation with the wordings in the sutra given by ashtadhyayi in the section defining the term karaka. We shall now study the link of these karakas with the actual words known as vibhakti in the next lecture. Thank you for your attention.


Hi everyone, Joe for Jasper's casewrakes.com coming at you with a quick little spot randomizer video I said the last six spots the final six spots of this mini box break that sell We're gonna give away an extra spot right there. So big. Thanks Jeremy Chad and Michael for getting in. Oh That's from the other break. Let's get some new dice new list and name on top after three We'll get it. We'll get an extra spot and then we'll see you in the next video for the for the mini box break All right, good luck. Thank you one two and three Three times. It's Jeremy Port Trying for the second time it finally worked this time Jeremy. Thank you. I'll see you in the next video for the break itself Jasper's casewrakes.com


Hey guys, we're back with some more simple kid-friendly art ideas and today We're gonna work on some art journaling if you remember yesterday. We did all of these little pieces right out of our scraps of paper with just crayons and pen and Then we ended up making some cards right that we could put a nice note into friends that were missing while we're you know Trying to stay at home and keep ourselves and everyone around us safe, right? Or we could send them to people who are sick in the hospital or something like that, right? So we made cards with some of them yesterday. I really like this card by the way Today I'm gonna show you something a little different you could do with those same pieces and I am going to do it in my art journal now art journal is a Notebook it could be any any any book That you do art and drawings Sketching painting anything like that in this is mine. This is not a blank notebook. This isn't actually an old calendar It could be a composition notebook something that you have at home It could just be a blank piece of paper. Maybe you don't have a book Just get out a blank piece of paper and you can work on that Use what you have we're trying not to go out right and buy anything new and We're trying to be safe and do what everybody is recommending, right? So these are just some pages that I have in here. I Have this piece of paint this page two pages that have some paint in the background And I've already glued down some feathers and things like that. You maybe have a blank piece of paper Maybe before you get started worth anything else. You just want to color with your pencils or your markers or crayons Put some color down. Maybe Take a pencil regular pencil. This is a this is a Mechanical pencil, but it's just a regular Graphite like school pencil and you could just make shapes and you could just do some scribbling You could do some writing And write about you know the frustration you're feeling about being stuck at home, right? And just make some marks on the background It doesn't matter what they are and then I'm going to take some of the pieces that we made yesterday and Again, I've just got our Elmer's glue. You could use a glue stick, which I actually have a glue stick right here I'm not sure which one I'm going to use right now, but I have them both out And I'm going to use some of our pieces and put a face on my page I'm going to use some of these pieces so I'm going to use my blue eyeballs and Blue isn't appropriate color because we're all feeling blue and a little bit frustrated and depressed about what's going on, right? So blue is a good color. So I'm going to just glue them down I'm going to get one of my popsicle sticks that I had yesterday and I'm going to use that to help me Push the edges down so things will stay in the glue, right? They'll stay glued down Put the eyes as far apart as I can on this page But if they're too and it being too close together for reality, that's okay. I'm not shooting for reality So I'm not trying to make a realistic face here. Okay, so I'm going to glue on my eyebrows And I'm going to now I'm going to put the eyebrows so the front part of the eyebrow Between the eyes is kind of up if you look in the mirror when you're mad It would be down if you are just calm It would be straight and if you're kind of worried or upset it would be up So we're going to put them up a little bit, right? Waste in the background is my family or the dog or somebody Well, just FYI. So we're going to put our eyebrows facing up We are going to grab one of our noses I'll use this one. It's right on top Put it on here about right there Put on some lips I have this arrow that happens to be the same color blue as the eyes I'm going to put it across the lips this way.  We've got our face on there I have a white crayon. I don't know how well it's going to show up on here But let's find out because this is acrylic paint in the background. So Yeah, so it's not showing up very well, but that's okay How about if we use the black Pen now you could if you are old enough to use a Sharpie and you have mom and dad's permission You could use a Sharpie on this that would show up really well Otherwise just grab a pen ask mom and dad if there's a pen you can use And just you want to just scribbly You know draw the shape kind of hint at the shape of the face If you do it with Sharpie see it shows up a little better If you have a big fat, maybe you have a dark flat black Crayola marker. No, this is not a flat one. This is a Sparkly one, but this I bet this would work too Yep It's not quite as black as the Sharpie, but it would work. Okay then I want to put words and I have a lot of words that I cut out from different things But I could also just write words on one of my scraps of paper So put that aside and let it dry for a minute I'm gonna take my scrap of paper one of the ones same kind I used yesterday. It could be any paper and I'm gonna write words in this case I'm gonna write words about how I'm feeling right now about what's going on in the world, right? So I'm going to write Let me see let me see One thing I usually try to do is make sure I'm spelling my words right because I'm not sure that one's actually spelled right So we'll hang on a second. I was right. It has an E on the end Okay, so maybe you want to just write after you make your Picture on your page you want to just write I'm confused or you want to write I'm happy to spend time with my family or whatever you're feeling about what's going on right now That's what I want you to write to put on your page And that's the thought that I want you to have when you're working on your art journal page Yeah, so now I'm gonna cut these out Again, we're gonna use scissors, but I want you to be mindful if you're young of making sure you have appropriate scissors for your age and mom and dad's permission and if you're not old enough to use a pair of scissors Then I want you to have mom or dad cut them out for you, right? They can help you do the writing too if you aren't sure about doing the writing or how to spell the words That you want to write have mom and dad help you Now you can see I'm taking my sentence and I'm cutting each one of the words apart I do this a lot and I save words like out of magazines and newspapers to do this kind of thing with and we'll be working on Something that like that in a future video. So now I've got all my words So we're gonna push them to the side and bring our journal back and grab our glue and Start putting them down. You can find them all first. Where's that? I? Lost one already. Yeah, that happened Seriously, I lost one already I don't understand how these things happen to me. Let's see. Oh, it's because it's stuck to my hand Okay So I'm gonna do this. I'm gonna just glue them down In a kind of a way that kind of makes sense, but it's messy and makes messy sense You'll see what I mean real wait. I am Put the words down in the wrong order helps if I do them in the right order. I am feeling You already forgot what I wrote. It's a nice thing about Elmer's glue is it doesn't dry right away So if you do that you can pull it off There we go If you have some parts that don't want to stick put some more glue hold it down for a minute There now you could do more to the page if you want but you don't have to It can be as simple or as complicated as you want it to be. I think this is Just enough it gets my point across. I'm not actually on the verge of falling to pieces Let me put that out there. I maybe was a few days ago, but I'm actually doing okay and Feeling okay about what's going on in the steps that we as a family are taking so Anyway, that being said, I hope that You all are enjoying your time at home with your family and that you're having fun making some art Do some art journaling. Don't be afraid whatever you're feeling to get it out in your journal Have fun with it and see where it takes you. I will See you in the next video. Don't forget to like share and subscribe if you can I'm not sure about this kid Video thing so what you can you can't do? But the most important thing is to have some fun Enjoy your space. Enjoy your family and do something nice for yourself because you deserve it and I'll see you later Bye guys


I will start giving you some details on the data we use and some relevant definitions. In our study we use mainly two data sources. First we work with microdata from SEDALAC for 16 Latin American countries from approximately the year 2000 to 2012 or 13, depending on the data that was available when we started with the project. That means that we have been working with more than 150 household services that cover more than 5 million households and 18 million people. The great advantage of this data set is that the household services are processed using the same methodology and that allows us to construct indicators and make cross-country comparisons. Second, we use also some macroeconomic data at the country level. Coming now to the labor market indicators, throughout the study we use 16 labor market indicators. We separate them in two big categories. We have the employment and earnings indicators and the poverty and inequality indicators. So we have, first the unemployment rate, then a big group of indicators related to the job mix. They are we differentiate between the job mix by occupations, by employment position, by the economic sector, by the educational level of the workers, and by whether the workers are registered with the social security system or not. Then we have the monthly labor earnings in real terms, the extreme and moderate poverty rate measured by the international lines, the shini coefficient of the household per capita income, and of labor earnings. What we do is for these 16 labor market indicators and for each of the 16 Latin American countries, we calculate the change from the initial to the final year of the period under study. We assign to each of the labor market indicators and evaluation criteria to decide whether the change was in the welfare improving direction or in the welfare reducing direction. So for instance, we will say that the unemployment rate moved in the welfare improving direction if it fell over the period and the difference was statistically significant at the 5% level. And then as a way to summarize all the information of this large number of indicators, we construct an index that we call C and is defined as the percentage of labor market indicators that moved in the welfare improving direction in each of the countries, considering only the differences that were significant in statistical terms. So throughout the presentation, we'll be analyzing changes in the labor market conditions. Sometimes we will use the summary index C as the dependent variable. And other times we will use the change in each particular labor market indicator. Now we move to the interesting part, to the changes in the labor market indicators and the economic growth rate in Latin America during the 2000s. So the first question we have in our study was a very simple one, from beginning to end, how GDP per capita and labor market indicators change. And what our evidence indicates is that for the average of the region, GDP per capita increased and all labor market indicators moved in the welfare improving direction. And when we perform the analysis country by country, what we found is that in each of the countries GDP per capita grew and most of the labor market indicators improved in all of the countries except for one. And next I will show you some supporting evidence for some of these findings. What we have here is the ranking of the annualized GDP per capita growth rate for each of the 16 countries. What we can see very easily is that GDP per capita increased in all of the countries. Then we have a group of countries that had a very large growth rate by Latin American standards. Those countries were Panama and Peru. Then at the other end of the scale, we have a group of countries that experienced comparatively low GDP growth rates. Those countries were Mexico, El Salvador and Venezuela. And if we calculate the average for these 16 countries, what we have is that the region grew at almost 3% a year. And that rate is well above the average for the OECD region and the growth rate of the United States. Now what happened with the labor market indicators? What we have in this figure is the summary indicator C. That is the percentage of labor market indicators moving in the welfare improving direction for each of the countries. What we can see here is that for all of the countries, but one, there was an improvement of at least 63% of the labor market indicators. The only exception here is Honduras that improved only 19%. And if we move this threshold a little bit to the right, what we can see is that for all of the countries, but three, there was an improvement of 75% of the indicators or more. The exceptions now besides Honduras are the Dominican Republic and El Salvador. And we can also see here that we have three countries that improved all the 16 indicators during the 2000s. Those countries are Bolivia, Brazil and Peru. So what this evidence is showing us is a very successful story for the Latin American countries during the 2000s. And now drawing on two of the 16 country papers that Gary mentioned at the beginning, we prepare a comparison of the GDP changes and changes in the labor market indicators for two of the most successful countries. Those countries were Bolivia that improved all the 16 indicators and Panama that improved 15 of the 16. So these countries were similar in terms of improving the labor market conditions and reducing poverty, but were different in their growth experience. Panama had the largest GDP per capita growth rate in our sample of 16 countries and based its growth on the provision of services to the rest of the world. And Bolivia had a growth rate that was closer to the average of the region. Bolivia only grew 2.2% a year and based its growth on the hydrocarbons and mineral exports. What you can see here is the growth experience of Bolivia. The GDP per capita increased 30% between 2000 and 2012. The growth was very low at the beginning of the 2000s and starting in 2003, the economic activity recovered led by the hydrocarbons and mineral exports. That was related to the increasing commodity prices and also to increases in the volume of exports because of some large investments in this sector. And during this period in Bolivia, there were also a new hydrocarbon law that was passed and that determined an increase in the royalty that is paid on the production of hydrocarbons and that means a large increase of fiscal revenues for the government. And the country was also benefited by a big reduction in the public external debt. And during the international crisis of 2008, there was a reduction in the commodity prices that obviously determined a reduction in the incomes from exports, a reduction in fiscal revenues and in the economic activity. But Bolivia was one of the few countries in our sample that continue having positive growth rates during the episode of the crisis. And that was related with the savings of a large portion of the revenues from hydrocarbon exports during the boom. And in the next year, as the reduction in commodity prices was only temporary, Bolivia recovered the previous high growth rates. And here we have a selection of some labor market indicators for Bolivia. We can see that there was a reduction in the unemployment rate with a mild and temporary increase during the international crisis, a reduction in the share of self-employed workers. That is very remarkable in Bolivia. That is the only country in our sample that started the 2000s with a larger share of self-employed workers compared to the share of paid employees. There was an increase in mille-level earnings that was not affected by the international crisis and a huge reduction in the moderate poverty rate that fell in half during the period. And now we have the evidence for Panama. The GDP per capita in Panama increased 82%. At the beginning of the 2000s, the growth was also slow. And starting in 2003, Panama experienced a real boom of the economy that was related to the provision of services to the rest of the world, like the Panama Canal, the International Banking Center, the transport sector and the construction sector. During the crisis, the GDP growth rate slowed down, but Panama was also a country that continued having positive growth rates. And in the following year, the country managed to recover the previous high growth rates because of some public infrastructure projects as the expansion of the Panama Canal. So the main conclusion from this... Sorry, we have here the indicators, the selection of indicators for Panama. We can see here that the unemployment rate has a big reduction over the period with an increase during the crisis and a posterior decline. The share of higher earnings sectors increased, and that was related with the role of the services sectors in the growth process of the country. Mi labor earnings also increased and the moderate poverty rate fell in half. So the main conclusion from this comparison between Bolivia and Panama is that these two countries were similarly successful in improving the labor market conditions and reducing poverty, despite having very different growth experience. So at this point, our hypothesis is that the GDP per capita growth rate was not the only factor able to explain the improvements in the labor market conditions, and it has to be something else. And that is what we have studied in our project, and we are going to show you now in the following slides.


So it's my pleasure to introduce you all to Dr. Nathan Kelber, who's the community engagement lead at JSTOR Labs and the director of the Text Analysis Pedagogy Institute. Having served as a public historian, research librarian, and professor, his projects promote technical literacy in data science and the digital humanities. Nathan's going to speak to us today on the topic of why learn text analysis, text and data mining with Consulate. Please join me in welcoming Dr. Nathan Kelber. Wow, an actual applause. You don't hear those often with people all in the same rooms. This is great. Thank you. I appreciate you keeping it very brief so we can focus on the text analysis. So I've got two versions of the slides. There's one on business and data science and one on digital humanities and libraries. My background is in digital humanities and libraries, I would say. But I do occasionally teach as a professor at Wayne State University in data science and the business school. I was going to ask the people on chat to say where they're from. Maybe they'll give us an idea. Sure. It seems like in this room we have a librarian, an artist, computer science, and engineer. So that's pretty varied. Oh, boy. OK, you guys are challenging me. We're going to do digital humanities and libraries, but then I'm going to supplement. And actually what I'll do is I will share here is the link to the slides for business and data science. And then I am going to actually show. This is for DH and libraries. And we can share those slides, too. I am going to share these on my screen over here. I'll bring the chat over. So we're in good shape there. Yeah, let's present these. All right, so why learn text analysis? I was supposed to talk a lot about the consulate, but I'm a really bad salesperson. I prefer to actually teach people because I think that's more interesting. And then show you why consulate is useful and talk about my experiences teaching and some other people that I've worked with that are now using this consulate platform, which is now in its beta form to teach text analysis. But I want to talk a little bit about for people who are curious about text analysis or natural language processing, why would you even want to learn this? And what's the value in doing this? And how long does it take? These are the kind of the questions that I think are really important as you start on your journey. So this is a very high level view down on text analysis. But I'll leave it open if people want to ask more specific questions as we get deeper into the slides. So I've already shared the links to the slides. I'll talk very briefly about Ithaca, which is a not-for-profit organization. Sometimes when people talk about consulate, they call us a vendor because we're associated with JSTOR. And I'm like, we're not a vendor. We're not for profit. I've never worked for a for-profit agency, and that's one reason why I'm terrible at selling things. But Ithaca has four parts, essentially, ArtSTOR, which essentially provides sort of like JSTOR, except for images. We supply 2 million plus high-quality images and digital assets. Ithaca SNR, which is like a research arm which libraries often use to make decisions. JSTOR, which you're probably familiar with, that is a not-for-profit digital library of academic journals, books, and primary sources. Importico, which is a preservation service. It's a dark archive where we preserve mostly journal materials for the long term. And so those are the kind of four different parts. I work in a special wing of JSTOR called JSTOR Labs. And it's kind of like an applied digital humanities think tank. It's the way that I figured out that I can basically be a professor and do digital humanities projects all the time that are new and interesting and not have to be in committees all the time. It's my secret professor world. And so we partner with publishers, libraries, and labs to create tools for researchers, teachers, and students that are immediately useful and a little bit magical. Here are some of the things that we've been working on. And when you look at some of these projects, you understand that we're not-for-profit, especially working on things like higher education in prisons with the Mellon Foundation and finding ways to basically help people teach and learn in all sorts of different institutions. The other thing that I'll mention here is the Text Analysis Pedagogy Institute, which I am the director of. It's an NEH-funded institute to help people learn text analysis and also to teach text analysis. Last year was the first year that we did it in partnership with the University of Virginia, both the library and also the School of Data Science there. In 2022, we're going to run it again in the summer and it will be in partnership with the University of Arizona. Last year, we brought in over 200 people virtually to basically take text analysis courses. Maybe I should supply a link to that because there's open educational resources that we created as a part of the TAP Institute and they are copy-left licensed. So if you want to reuse them or adapt them for your own learning and teaching, it's a great place to start. Stuff like Python basics, introduction to our optical character recognition, topic models, pandas, machine learning, visualizing humanities data, text analysis in ancient and medieval languages, and named entity recognition, those were the courses from last year. And right now we're just getting the instructors together and we'll probably put out a call for participants maybe at the very beginning of 2022. It may be late 2021. So if that's something you're interested in, keep an eye out for that. And it's completely free. It's funded by the NEH. Normally I do a level set, but I think I've gotten a little bit of sense of who I'm talking to. It seems to be a fair number of students actually as well. Am I reading that in the room? The box is a little small. You have everyone from undergrads to grad students to faculty members here. Great. I always watch what the grad students are doing. People think like, look at what the faculty are doing, but like the grad students, I'm like, what are grad students talking about right now? That's how I know like whether I'm on the pulse of what's cool right now. If grad students are showing up, then I know I'm like, okay, I think I'm doing the right things right now. I'm doing the things that are interesting. Well, I know I didn't. Why first of all, why would you learn text analysis? First of all, data literacy is really at the heart of research. And this has been true for a long time in the STEM world, but I think it's only recently become true in the digital humanities kind of over the last 15 years. So as humanities computing transition into digital humanities, data literacy is really changing. It's really changing the way that we do research and the way that we kind of search, find and discover information. Data skills are really in demand right now. If you were to look at Glassdoor and figure out like what were the kind of top positions for the last five years or so, what do you think that position is? It's not English professor, unfortunately. I wish it was. Data scientists? Data scientists, you're absolutely right. I think it fell to number two this year. So I may be a little bit out of date there, but data scientists is the top career on Glassdoor. But everywhere, data skills are in demand, especially in libraries, as they're trying to work with bigger and bigger data, they're trying to work with faculty that have all sorts of different data needs. I worked on a project recently as part of a grant on collections as data basically looking at how can libraries begin to think of collections as data and to collect data in such a way that it can be used for research. And I think that learning text analysis is also really important because it helps us understand things like algorithmic bias. And here I'm gonna take a more humanities bent. That is even if you don't expect to become an expert in text analysis, even if you don't wanna deploy machine learning models or whatever, I think it's really important to understand kind of how some of this stuff works, if only because it's already making decisions in your everyday life, whether you're aware of it or not. That is, models are being used to decide things like which neighborhoods will get patrolled more often by police officers or who gets a particular loan from a bank or questions about who gets paroled or who gets surveilled or whose information is available and whose information is not. So I think that it's really important to understand text analysis and the ways it's being employed so that we can be aware of issues like algorithmic bias. And I always keep a copy of data feminism because I reference it so much and I recommend it so much. So I have a quote here from Data Feminism. You haven't read this, humanities people and also data science, frankly. It's really interesting work, looking at kind of why why ethics in data really matter and why we should think about data justice. And I always stress this when I teach data science strategy and leadership is really thinking about issues of ethics and justice when it comes to data. So here's the quote I have, decisions of civic, economic and individual importance are already and increasingly being made by automated systems sifting through large amounts of data. For example, Fred Poll, a so-called predictive policing company founded in 2012 by an anthropology professor at the University of California, Los Angeles has been employed by the city of Los Angeles for nearly a decade to determine which neighborhoods to patrol more heavily and which neighborhoods to mostly ignore. But because Fred Poll is based on historical crime data and US policing practices have always disproportionately surveilled and controlled neighborhoods of color, the predictions of where crime will happen in the future look a lot like the racist practices of the past. And this is also discussed quite a bit in the book, Weapons of Math Destruction. That's M-A-T-H Math Destruction. So I recommend checking that book out as well. I've got a second quote by Ruhab Benjamin and this is from Race After Technology. She says, the same sort of algorithmic filtering that uses more ethnically tailored representations into my feet can also redirect real estate ads away from people like me. This filtering has been used to show higher paying job ads to men more often than to women to charge more for standardized test prep courses to people in areas with a high density of Asian residents and many other forms of coded inequity. The difference is that coded inequity makes discrimination easier, faster and even harder to challenge because there's not just a racist boss, banker or shopkeeper to report. Instead, the public must hold accountable the very platforms and programmers that legally and often invisibly facilitate what she calls the new gym code. And this is the way that kind of racism is written into coded language. And often this takes the form of a bias in training data sets. So those are some reasons not just why you might wanna learn from like a research perspective, but also just like a general like good citizenry perspective, like it's good to have an understanding of the way that these technologies can be used for good and also for evil and to think about critically how technologies are being deployed in your community however that is. And so but if we look at it from a more kind of academic perspective, why would you wanna learn text analysis if you were a humanist, for example? I think it's clear that there are many humanities faculty that will never use text analysis. But I think that's slowly changing over time. And part of it is because the way we do research is fundamentally changing. And here's Dan Cohen at Northeastern. Says many humanities scholars have been satisfied perhaps unconsciously with the use of a limited number of cases or examples to prove a thesis. Shouldn't we ask like the Victorians, what can we do to be most certain about a theory or interpretation? If we use intuition based on close reading for instance, is that enough? Should we be worrying that our scholarship might be anecdotally correct but comprehensively wrong? Is one or 10 or a hundred or a thousand books an adequate sample to know the Victorians? What might we do with all the Victorian literature? Not a sample or a few canonical texts, but all of it. And in that realm, I think that humanists are increasingly confronted by the fact that they have data. We're not generally creating data through experimentation or observation. More often than not, we're mining data from historical documents. You name it, we've tried to mine it from whaling logs to menus to telephone directories. This means that we tend to want different tools than scientists. And also that we have some interesting data wrangling problems. More often than not, the categories that our historical sources use to divide up our data are not the same ones we're interested in analyzing. So we often have to do some very creative transformations and interpretations. This is a really great piece by Miriam Posner that I recommend when people are starting to think about humanity's data and why analyzing it and going through it may be very important. But put very, very simply, if you cannot read, manipulate and interpret data, you're overlooking an incredibly rich source of understanding the human condition. And you need to know kind of like what can be discovered in data and especially data at scale and how data at scale can be used. And as I've been alluding to as well, how it can be misused. I think that's equally important from a humanities and kind of social justice perspective. Here's Harriet Green speaking particularly about the issue of collections as data. And this kind of mimics my experience. This is for the librarians. This mimics my experience at UNC where we had like these huge collections and they just like they were given to us by a vendor in a bunch of hard drives. And then we kind of put them in the closet because we didn't know what to do with them. They weren't ready, they weren't show ready. And so she says, not all of the data we create or purchase for library collections comes in neat multi-gigabyte packages of quarter files. We recently discovered that datasets we had purchased as part of a database licensing negotiation for more shelf ready than machine ready. They currently exist as stacks of hard drives, disks and other bewildering formats sitting on a book cart. How do we provide access to these data collections? And so we're all awash in data. I mean, that's really what this is about. It's that there's tons of data. We don't know how much information is in it. We don't know how to get the good stuff out of it. And when it comes to text analysis in particular, that is the vast majority of the data. That is when you go through a data science program, a lot of it will tend to be focused on numerical data, things like linear regression, logistic regression and kind of working with numbers which are famously tractable ways of working with computers. But less is understood about working with words or strings or working with unstructured data. That is data that's not in tables or Excel files and kind of like how do you take five million emails or five million tweets or five million novels and get something interesting out of it or useful out of it? I think that's a more difficult question, but when we actually look at the makeup of the data that is out there in the wild, the stuff that's being created all the time, it's mostly unstructured text data or things like, there's also things like obviously audio and video which people are often trying to get to text materials. They're using interpreters and other types of things to try and get to the text data to analyze things further down the line. And so the question you should ask yourself if you're thinking about learning this stuff is first of all, how long, how long is this gonna take me to learn text analysis? It's kind of like when I was an early modern scholar, I was like, oh, crap, do I need to learn Latin? Am I gonna be in trouble if I don't know Latin and spoiler alert, I didn't learn Latin but many of my colleagues did and they did not learn all of Latin. That is they only had to learn enough Latin to kind of get by with the types of research they were doing. Nobody, it's not possible to learn all of text analysis. It's kind of like a silly thing to be like, I don't know, learning all of English or all of history or something like that. You can't do that. And so like, what you need to do is you need to know like, here's the kinds of data that I may have access to and here's the kind of questions I'm interested in. And so like, you just kind of need to know some of the affordances like, what are the some of the methods? The problem is most people aren't equipped to answer that question. They don't know like the difference between say topic modeling and Latin deerslay allocation. Like those are just weird words. And so like, my goal here today is to introduce some of these opportunities really about what you could learn and what might be the opportunities for your research. In text analysis, obviously. And so I wanna kind of give you that guiding path, that kind of initial step. But before you can get started, you're gonna need some data of course. And really you need machine readable data, machine readable text. That is something that can be copied and paste. You can start with something like PDFs or pictures or something like that, but then you'll have to do some type of optical character recognition to get you to that step where you can copy, paste text. So you need machine readable text and since this uses statistical methods, you're gonna need lots of it. And this is the part where a lot of humanists just check out. They're like, well, I don't have that. So that's the end of this talk. And so part of the goal with this Consolidate program is, hey, we're a nonprofit that has access to tons of machine readable text. Maybe we can help people create interesting data sets that they can learn either doing research or just using it for exploratory purposes in their discipline to kind of learn some of these techniques. And who knows, maybe publish a paper or something out of it. But the idea is to really give people all of the tools they need to be in learning these things, right? And so now I'm gonna talk about directions that you could go. And this is really kind of the bulk of what I want you to take from this talk other than you should check out Consolidate and it's really cool, but mostly this is the thing that I think is interesting from an educational perspective. I've got five questions here and each question has like several techniques and this is not exhaustive. This is just like a very quick introduction to some of the things that are out there, some of the possibilities that you could explore. And so I'm gonna list the five questions underneath you're gonna see the methods. And I'm gonna talk about each method a little bit more in depth, but first I'm gonna introduce these five questions. So the five questions that text analysis can really help you answer are, what are these texts about? And that's probably the most popular question is the question that gets asked the most. Like I have a bunch of emails, what's in these? What are these about? I have a bunch of novels, what's in here? I have these whaling logs, what are they talking about? So that's what are these texts about? The second question is how are these texts connected? The third question is what emotions are expressed? The fourth question is what key names can I find? And then the fifth question is which of these texts are similar? So I'm gonna talk about each one of these methods in just a moment here, but I just wanna introduce the five questions first. So the first question, what are these texts about? There are a number of methods that I would recommend right off the bat that are fairly simple to employ that will begin getting you thinking about kind of what's inside of these texts. And the first thing at the beginner level is just going through the texts and seeing how many times certain words occur, basically word counting, like putting all the words in a big bag and then pulling them out one by one, like a scrabble tile and just saying like this word occurs this many times. Sometimes this is called bag of words approach because you're not really concerned what order the words come in or what part of speech they are or anything like that. Just like how many times does this word occur? And that's a very good beginner technique to just get a sense like how many times a word occurs. So you could ask a question like which of these texts focus on women? And so you could look at the word woman and women and you could see like how many times do those occur in these texts? Of course, that may not be certainly it's a beginning exploratory technique is not gonna get you all the way there but it's like a first step in kind of beginning to figure out what the texts are about. Like what are the top 100 words? And here you may need to take out certain words that are function words that are likely to occur say in English that would be things like the and of or we call these stop words. And so you may need to pull those things out but the idea here is just to figure out what are the words that are the most common? And like maybe one level beyond that we can do something like co-location. Co-location is examining where two significant words occur close to another. They could occur like right next to each other or they could be like three words apart or you could say like give me or they could be like in the same sentence or the same paragraph but you're really looking to see like where those two things occur in close proximity. And so the example here is where are women mentioned in relation to home ownership? And so you could be looking at those two different things at home ownership being like could be like a two word construction. Sometimes we call that a by-gram that has two parts. And so you would look at, where are those two things co-located inside of a text? And you may actually pull those out and begin to like close read them or you may take a different approach depending on what your research needs. Another thing that you can do at a more intermediate level is topic analysis or topic modeling. And this is an unsupervised machine learning technique and that may sound intimidating but I can tell you that in the courses that I teach the workshops, we usually get to something like topic modeling within the first month. And that's for people who have never programmed in Python before. And so topic analysis is really asking the question what are the most frequent topics discussed in this newspaper? And it's just kind of going through and trying to group basically common topics or groupings of words together that are in a certain set of documents. Another thing that you can do is significant terms analysis. One form of this is TF IDF which is basically what powers search engines probably powers your library search. And it's basically finding the significant words within a text. And so you might ask a question like what language is most significant within these 1970s political speeches that I have? And so it's basically figuring out like what are the terms that are frequent in a given political speech but infrequent in other political speeches? So when it comes to something like TF IDF you're really trying to figure out what are the words that are common in this particular document that are not really spoken about in other places? And so it's not just looking up here word frequency it's also looking at well inverse document frequency. It's really trying to figure out like what are the words that are very common here but you rarely see other places and those are considered significant terms. The second question, how are these texts connected? And humanists have actually been doing these concordances for a very long time. I remember looking these things up in the library in like big thick books. But like concordance as a Shakespeare scholar this was very useful because you could look up like where a certain quote was repeated many times but it's really where's this word perhaps phrase used in a variety of documents. And so the example I have here is which journal articles mentioned Maya Angelou's phrase if you're for the right thing then you do it without thinking. So if you're like an Angelou scholar you might be curious what have people said about this particular quote. And if you could bring together all of the places where that quote has been written about that would be very useful for your research you would have the entire scholarly record on that particular topic, right? Another thing you can do is a network analysis. And often this is talking more about kind of information and knowledge flows and how people are connected. The famous digital humanities example of this is called the Republic of Letters but it's really kind of like who's talking with whom and how are those communities connected? And so if you had a bunch of different documents that were connecting different people whether they're letters in the case of the Republic of Letters or you could have emails or tweets or like it could be any number of things that are kind of like directionally oriented you could begin to ask questions like what local communities formed around civil rights in 1963? Or you could actually ask a similar thing with like Twitter data or Facebook data around the George Floyd protests or something like that. So the idea is to generate a network visualization of kind of who's talking the most and where they're talking with and kind of where the different pockets of the community are forming. The third question, what is this kind of author feeling? And this is actually perhaps more popular in business and data science. Sentiment analysis is really huge in business and data science world. Anytime you make a phone call and it says, this call may be reported for quality assurance purposes. What's usually happening in those cases is they're taking the phone call and then they take the phone call through a transcriber and actually it's amazing the transcription quality of the best models now is actually better than human transcribers. And so like they take that and they transcribe it into text and then they use a sentiment analysis to get a sense of the sentiment. And it could be something basic like somebody happy or angry or confused or it could be more sophisticated. There are different ways of doing this. Sometimes they use like weighted models where they say like this word has a positive sentiment and it's weighted this way and it has this negative sentiment and there's all sorts of different kind of procedural ways to do it where you can make sure that you're accounting for things like negation, like this is nice versus this is not nice. And even models that will there are ways to analyze emojis and amodicons and I wouldn't be surprised if somebody is doing sentiment analysis on memes at this point, that would not surprise me at all. But then there's also more sophisticated things where you can basically give you can train your own model where you're saying these are all the people who had negative things to say and these are all the people who had positive things to say and you train the computer based on those identifications that have been labeled by others. Question four, what key names can I find? And this is really named entity recognition. It's basically listing every example of a kind of entity it's gonna go through your text and try and figure out like who are all the people that are being named in this text? What are all the locations? What are all the dollar amounts? What are all the company names? Like it'll go through your text and try and identify things of interest to you and pull those things out. And so it's an extraction technique really is pulling out the entities that are of interest to you. And this could be useful if you're just trying to figure out maybe you're looking at your field and you're trying to figure out you pull out all of the names in all of the proceeds from a particular conference the latest conference in your field and you pull out all the names. That would be interesting, right? Cause you would get a sense like who are people really talking about in the field? You could get a sense of like the trends or what are the, you know, discord? What's the discourse? What's the jargon? What are the terms that people are using? So this named entity recognition could help me do that kind of analysis. And the fifth question, the last question is which of these texts are similar? And this is used in a variety of different ways. You can do things like clustering where you're really asking kind of really which of these are most similar in a stylistic way. So there's a field called corpus stylistics. And so in like Shakespeare's studies people ask questions like is this play closer to a comedy or a tragedy? And they'll figure out how things cluster. And there are different ways to do this clustering where you can do hard clustering where you draw like a hard line between the different clusters and do fuzzy clustering, which I always find funny cause I think it's like portoroid clustering or something where you're like, there's some overlap. So when you do like a topic modeling with LDA then some of the words that are in different topics can overlap when you think of like a Venn diagram they can overlap between two different topics. And then there's also hierarchical clustering which is kind of like a like dendrograms basically. They're these kind of like Rudy images that kind of show let's say you, I did one on Chaucer's tails and the Canterbury tails probably 10 years ago, wow. And it basically starts from the bottom up with all of the tails and then they kind of form into these groups and you see like which tails are closer to each other in terms of their stylistics. So that's a little bit about clustering. Another way you can do clustering is with things like supervised machine learning. And so we identified texts that are similar to a certain thing and this is where when you get into the area of supervised machine learning you're actually using pre-labeled data. And so in this example here as part of a project I helped start at UNC which is now looking for a project manager. We basically took the work of Paulie Murray who wrote this book in 1950 called States Laws on Raising Color. Don't get me talking about Paulie Murray because I'll get way too excited. But she wrote this book and like Thurgood Marshall called this book the Bible of the Civil Rights Movement. And basically she was attempting to find racist laws in the book. And so we took all of the laws that she discovered and we used them to help train our model along with other experts, Dr. William Sturkey and Kimber Thomas who was a clear fellow at UNC and a few other folks. Basically had them label these laws. They looked at these laws from the end of the Civil War through the Civil Rights Movement, 1860s and 1960s and determined whether they thought they were Jim Crow laws or racist laws and basically put them in a pile yes, no or maybe. And then we used that to train the machine learning algorithm to go through basically all of the laws. I mean, we had, we scanned all of the volumes and it took us over a year to put together this corpus of all the laws in North Carolina. Now, Paulie Murray did it. She, I mean, she didn't read all the laws but she did it for every state in the country not just North Carolina, every state in the country in 1950. She didn't have Google and so like we were basically trying to discover more racist laws and we found over a thousand of them on the books. And so that's an example of supervised machine learning. Lastly, the authorship attribution which is occasionally common. Common example in digital humanities is the Federalist Papers. But I always mention J.K. Rowling's book, The Cuckoo's Calling, which she wrote under a pen name. Nobody knew who wrote it but there was a fellow Patrick Duola and he did this authorship attribution. He basically said, J.K. Rowling wrote this book and she was forced to come out and basically admit that she had written the book under a pen name. So authorship attribution is basically using statistical analysis to determine who wrote something. So those are the five questions. What are these texts about? How are these texts connected? What emotions are expressed? What key names can I find? And which of these texts are similar? Now I know that's kind of a whirlwind of information but I think what it does is at least, I hope what it does is demystify a little bit some of the very common methods in text analysis and natural language processing that might be used by people in libraries or digital humanities. One area where I'd like to develop some more material is, I have a lot of librarians that are interested in training machine learning models for classification or things like subject headings and basically trying to like read through a document and figure out the metadata that should be associated with it because it's incredibly valuable work and it's incredibly laborious. And so I often get librarians asking me about that and maybe that's something I'd like to develop in the future I think some type of course on that. But there's the link again. There's my email address, there's the Twitter and then a link to consulate.org which I'll talk about in a moment here because I wanna give consulates due because I'm supposed to be telling you about this platform that helps teach and learn these things but I wanna pause because I wanna see if folks have questions. Nathan, I have a very practical question. So I'm super excited about the possibilities of text analysis especially because we have so much OCR and text right here in the library. To go through and systematically learn those is that what your courses would do like in early 2022? Um, so I wanna clarify a few things. First of all, I've written a series of notebooks and the things that I teach for consulate kind of train the trainer type things very similar to the carpentries or programming historian or things like that. At the text analysis pedagogy institute which is an NEH Institute that I will run next summer with University of Arizona. I will teach one course in Python basics probably but there are a bunch of different teachers that teach all of these different areas and we're always kind of looking for feedback about what people are interested in when we recruit the teachers to try and kind of build out the open educational resources because frankly every university in the world either and I don't think this is hyperbole every university in the world either has somebody that is teaching this stuff or is thinking about teaching it and developing materials and trying to figure out how to teach it. And so like that seems silly to me. We should all be sharing open educational resources rather than redeveloping the wheel at every university in the world. And so like the way I see it, we should be kind of while we should be forming communities obviously but I think that's even challenging at a local scale like at the University of Utah, do you know the people who are interested in doing text analysis? If you were to, you know, like that was very hard for me to figure out at UNC when I was the digital scholarship specialist. It took months and months of work and when I was done at UNC then I started working at the larger triangle level with the digital, well, it was called the Triangle Digital Humanities Network but then it then expanded again beyond the Triangle Digital Humanities to be North Carolina Digital Humanities Network but it's an incredible amount of work just the networking work to figure this stuff out. And so my like ideal vision in an ideal world we would be like building a community of people who are like sharing open educational resources and like it's not just me teaching people and kind of teach the teacher's kind of way like, but it's like a community of people like sharing educational resources and making it possible for people to meet the demand. And like the demand is huge. When I was at UNC, which was over two years ago now, we would have waiting lists of over 80 people for things like Intro to Python and Intro to R. We had Barbara Rachenbach recently speak at Ithaca who at the time was the AUL at Columbia. Now she's the university librarian at Yale but she was saying essentially like they would offer these courses and they would fill up within minutes and they couldn't meet the demand. The only thing that saved them weirdly enough is the pandemic and they had to go virtual. And so with the virtual teaching suddenly they could teach like huge amounts of students. And so like to give you an example at probably our most successful example in our beta with Consulate at Northeastern University, they've been teaching these kind of classes for Intro to Python based on my notebooks. And I was like, you know, you guys have been doing really well with this. Like how is it going? And they told me last week that they have a waitlist of 324 students for this course. And I was just like blown away. I was like, wait, what? I thought it was crazy that, you know, at the TAP Institute, we had 200 people. We had people literally from all over the world, all over the world. And like the Ithaca people were coming to me like, should we keep promoting this or we wanna promote it with this and this? And I'm like, no, no, no, stop. Like stop promoting this Institute. We can't review, you know, this many applications. We don't have the like resources to review so many applications. And I was like, shut it down. So I think there's just huge, there's huge need out there, especially at R1 and R2 institutions. Although at the same time, the smaller institutions are the ones that could kind of use these things the most because they don't have the IT infrastructure to run their own Jupyter Hub and kind of facilitate all this teaching. So, yeah, I think there's huge need, but the bigger problem often on campuses is people just don't know where that need is at. And this like little grouping here is an example of that where I just go in and I'd be like, who's here? Cause like I thought it was gonna be DH people, but it was computer science people. It was business people. Like I just don't know like, I never know who's gonna be in the room. And so there's part of it, a big part of it for any university is just figuring out like, how much demand is there and like where are these people coming from? Because they exceed the capacity of any single Liason library and to like meet with all of these people, right? Like they're in all different disciplines and all different parts of the university. And so it's a huge problem to try and meet demand to build the community out. I know that was a long winded answer. That was great. Thank you. Other folks have questions. I can give a brief showing of Consulate. I know I'm supposed to be like, maybe you can have me back and I'll do like a real demo of the platform, but I'll show it a little bit, but I wanna see if anybody else has questions. I have a quick question. How much math do you use with this text analysis? I guess what are some concepts in that that there are statistics? Statistics are very helpful for understanding the models. I often work with digital humanists, many people who have never coded and they're often afraid they're like, math is not my subject, but when you look at study after study, like what makes us somebody good at learning to program, it's actually language. It's not numeracy, it's literacy, like language comprehension and logic. And so like, I feel like a lot of humanities people get tripped up and they're like, I can't learn Python, like this is, there's gonna be a lot of math and this is gonna be really difficult, but we start with the very basics, like the very basics. And that's why I start with Python basics and just introducing Jupyter notebooks and things like that. And part of what makes this approachable is basically the way that Consolate allows people to teach. It allows people to teach without spending a ton of time setting things up. So when I was teaching these things at UNC and when I taught them in other places, it can take a couple of hours or more to get everybody set up, especially if you have like a workshop with 20 or 30 participants and half of them have Macs and half of them have PCs and like they end up having different issues or somebody has old installations or there's path issues or there's a million different things that can trip you up when you're just setting up the environment to begin learning. And so the nice thing about Consolate essentially is you give your students a link, everybody clicks the link, it doesn't matter if they're on a Mac, on a PC or on Linux, like it all goes to the same place. Everything is run in the cloud and they have an identical environment where they can begin to learn quickly. And so that's ideal for kind of running these workshops. Now, how much math is really involved and with the kind of like Python basics stuff, I don't think it's a ton of math. A lot of it is kind of more like logic and flow control. That's the hardest part I think. Like I often explain this with flow charts and things like that. If you wanna understand some of the mathematics behind some of the algorithms, you can get incredibly complicated, obviously. But at the more basic level where you're kind of learning to deploy these things, I don't think that you need to know a ton of math to begin programming in Python and begin doing natural language processing. A lot of the libraries have already been written. And so really you just need to learn how to write Python code and to deploy these libraries in appropriate times to do the analysis. Now, as you become more advanced, you may become more curious. And so like for things like TFIDF, the math is like really not that complex. And so I go, I explain exactly how the math is done in TFIDF, we do it on like calculators outside of the notebook. Like we just like punching the numbers. And so I'm like, you just need these three things. That's all you need, it's not complicated. But to do it at scale with like millions of books is when it seems more complicated. And so if you're intimidated by the math, I don't think that should be the case. The thing that should intimidate you more is it takes time to learn these things. And so you should be strategic about what you wanna learn and how much you wanna learn and how much time you want to spend on certain things because there can be very deep rabbit holes where you're like, well, I wanna learn how to do this and this and this. And there's an infinite amount to learn. I mean, this is why we all work in the academic world because we're addicted to learning. And so that's something I think that requires more critical thinking is like, not can I learn the math, but really like, is this the career trajectory that I want? Is this the direction I wanna go and why and how? I think those are more important questions than yeah. How much math do I have to do? Other folks, we're only slotted for an hour, right? Yeah, we may have to do a follow-up consulate training at some point. Sure. I can show you very briefly. I can show you very briefly. Whoops, did I stop sharing? Oh, no, I didn't look at it. So there's two sides to this consulate platform. One is that we have an ability for people to build data sets. And so we have this amazing data set builder that allows people, I'm gonna clear all the filters, allows people to build data sets based on a variety of content, including JSTOR, Portico, which is that dark archive that I was talking about, Pranay Kling America, which is a newspaper collection created by the Library of Congress. Doc South, which is documents of the American South collection created by UNC Chapel Hill. The South Asian Open Archive, which is a bunch of really amazing materials on Asian culture. And it's really kind of neat because it works in a variety of different languages so you can build things out that it's cool when you see all the different word clouds in Hindi or Bengali or all these different kind of languages that are part of this collection. The reveal digital content is also newspapers. As of right now, it's like dissident newspapers from the 1960s to the present. Alternative press, radical newspapers. There's also some prison newspapers, which I believe have actually been added in there now. But it works somewhat like doing a regular library search, but you're building a data set. You're not just looking for one thing. You're like trying to build a whole data set. You can also filter by publication titles. So there's like a particular journal or set of journals. You can build that out. You can also browse the newspapers or journals like literally one by one to build your data set out. And obviously the bigger your data set, the better for kind of doing text analysis work. It will automatically generate some outputs, some visualizations that you can kind of tailor. And you can save these and share these and download them and download the data behind them. There's a term frequency viewer. There's document categories of overtime, tree maps. This is a preview of the documents that are in here. And then when you're done, you just build the data set. And any data set that you've built, you can also download up to 25,000 items. And we have ways that we can get you more than 25,000. There are ways to do that. But like self-service, you can build a data set up to 25,000 items. And you can download the metadata. You can download unigrams, bigrams and trigrams. That's one word, two word and three word constructions. The word counts for those basically. For any of these, except for JSOAR and Portico data, we're also able to share full text. Now, because of copyright law, we have to follow the law. We are not able to supply full text for the JSOAR and Portico data. But there's a tremendous amount of stuff you can do with just the unigrams, bigrams and trigrams. And then speaking of tutorials, these are the lessons that, well, some of the lessons that I often teach. So getting started with Jupyter notebooks, these Python basics, there's a set of three of them here that go through the basics of Python, metadata, work frequencies, TF-IDF, pandas, working with stop words lists, data set files. Here's sentiment analysis. And sometimes we do topic modeling or tokenizing and things like that. But these are, I'll show you actually, I'll show you in the learning environment just real quick, this'll be the last thing. You can open this immediately in the Consulate Lab and you can run code in the browser. So this is what makes it amazing for teaching and learning is that you can essentially build a data set and then load it into the Jupyter notebook environment and then run the code live in the environment. So you give your students like a link and then they all click that link and then that leads them into the environment where they can begin doing analysis. And so if you're familiar with something like Binder or Jupyter notebooks, that's essentially what this is, using Jupyter notebooks to help people learn in the cloud. Well, in case we need to lose people close to two o'clock, I do wanna say thank you so much for coming and talking to us. We really appreciate it. I know I've played with Consulate a little bit and it's a really great resource. Anyone have any questions for Nathan while we're all still here? So I had a question, Nathan. I'm not familiar with Consulate. So I'd be very curious to see a follow-up but just playing around with it initially, do you have plans and developing it further so that all the different modes of analysis that you covered in your presentation could possibly handle through one portal through Consulate? Certainly you could do all of those things and more if you were to write the code. I haven't written the code, but like the idea, I think is obviously I will continue to write notebooks and I will continue to teach. We're also hiring another teacher to come on board but it's a kind of train the trainer's model, right? Just like the carpentries. And so I'll continue to try and listen to what people are saying they want, what are the most desired things they wanna teach and learn and to develop those materials. That's true both for Consulate and also for the text analysis pedagogy Institute whose goal is really to develop those open educational resources to make it easier. And so like we do not have notebooks for all of the things that I mentioned in my talk but I would love for us to get there. We're still in beta so but yeah, this is the Jupiter notebook environment and this is basically there's some of these have video materials, some of them have explanations and links, there's a huge glossary of materials. This is a lot of people say this is their favorite part. I've created this huge glossary in our help documentation of all of the terms in text analysis and our help documentation is actually, I write most of this and it's quite expansive for all sorts of different things. So this is not like an unresponsive or like something that comes without documentation helpful. We spend a lot of time helping people and we figure out what the pain points are so that we can iterate. But the idea here is that I can write Python code in here and it will execute in the environment. And so people can learn to do any of these any of these different things by basically going through one of these notebooks and learning them. I'm working right now on a notebook that's on Optical Character Recognition. And so this is based on Hannah Jacobs' work at Duke University or she's also at UNC Chapel Hill for the TAP Institute. And so this is a whole notebook that intends to help people learn how to do Optical Character Recognition. But you can see here all the different notebooks that focus on a variety of topics. And of course, like I said, we're trying to expand on that. We're getting ready to hire a second teacher. So I hope that answers the question. Like we're not there where we've done all of the things that I mentioned in there. And the thing is there are different ways of doing those things. So like I have a sentiment analysis with data but there should also be a sentiment analysis using machine learning. And we have like topic model and using latent Dirichlet allocation but there should also be latent semantic analysis. And so like there's no way to like do everything. And I don't like, and part of this too is like a lot of these are built off of our dataset builder but people wanna analyze other things than what are in our dataset builder. So you can bring your own data into the environment and write new notebooks and new techniques. And so the idea that I think we're trying to facilitate really is really like how do we create a space where people can write all of these things and share all of these things both with their students or patrons but also with their, with other instructors and researchers. Just a quick follow up. Are you, is there a compilation anywhere of different syllabi that have used consulates and their environment and lesson plans? I don't think there's any such list at this point. We're too early. People are kind of developing on their own but we haven't, what we would ideally like to be able to do is to like gather those all into the platform itself and make it easy for people to discover it rather than having them in different places. So there are people who are writing like all of these different things. So for example, with the TAP Institute those are all in different GitHub repositories, right? And so like we could I suppose start creating just like a basic list that would attempt to gather all of the different repository links into one webpage. But I think the better way forward is probably for us to continue developing to the point where it's easy for people to share. So what we have right now in the environment at this point in time is people are able to sign into the environment if you log in and then you're able to modify notebooks and save them in the environment. But we're kind of working on the mechanisms for like how people navigate in that space and save their files and share their files. And so that kind of like sharing component is still very much being developed. What we have mostly at this point is the ability to create the data sets to run the notebooks and easily bring in data sets and to share the notebooks with links. And quite a lot has been built out but we don't quite have the part where it's like the sharing community of notebooks and open educational resources. So most all the things you'll see like on our website are things that I've either authored or co-authored. But if you go to the TAP Institute which use Consulate essentially for teaching the Institute, then you'll see additional things that people have developed. But again, this is an open standard we're using Jupyter Notebook. So they're not just things that will run on Consulate, they're things that will run on other platforms like Google Colab or MyBinder or other types of things Kaggle. And so our goal has always been to like develop things and as open a way as possible as a nonprofit. Okay, well, I think we are officially out of time but for everyone here in the room or on Zoom, we will be adding these links to the event page if we don't have them there already. And thank you again, Nathan for coming and speaking to our group. And it made me very excited to go play with Consulate. Awesome, and if you want me to come back and give you a real demo, I told you I'm not a very good salesman. Want me to do a real demo? I can show all the kind of ins and outs of the platform but begin to play with it and send me an email if you have questions or yeah, you want more information. And I'd be happy to come back if you want me to like do an intro to Jupyter Notebooks and intro to Python course. It may be worthwhile. That may help you figure out like how many people would be interested in it? Is that something you offer at the library? Not exactly. And I think there's a lot of people at the university interested in that because when you did your last, we had advertised through Digital Matters, your last round of classes for like intro to Python. And I think it was like a four week, eight session course. And we had seven people sign up from the University of Utah. So there's definitely interest on campus on learning these things. That's good because my network is very weak on the West Coast. So if we're reaching Utah then that's good. Well, you know, we're not quite a coast, but... I mean, like I said, I mostly, most of the people I've worked with in my 10 plus year in Digital Humanities is like very East Coast and Midwest, not mountain region. Mountain range. Well, yeah, I mean, I think that we'll probably take you up on that and we really appreciate it. I mean, I have so many more questions but I don't wanna keep people longer if they need to head off to class or other things. So... Great, let me know if you wanna run another session and yeah, let's spread it wide because I think what you'll discover is there's more interest than you thought. Yeah, exactly. Thanks, Nathan. Thank you so much. Thank you, Bob. Bye. Bye.


The WPC is a great opportunity to both hear at the panels interesting observations on what's happening in the world of economics, the world of politics, and also an opportunity to speak with people who are here who play a major role in both European and world affairs. In thinking about the politics and economics of the world today, I think for me at least as a scholar it's more important to understand different points of view. For example, as we heard today, what the point of view of the Chinese government and its supporters is with respect to events in Hong Kong or events around the world. In many instances these questions don't have answers. They have different perspectives that will eventually be bargained out in the world of economics or the world of politics. Yes, I'll bring back some good reminiscences of interesting conversations, of interesting debates, of the statement of position of people who have a major role to play in the way that the world economy and world politics will develop.