Good morning, and welcome to this week's edition of Encompass Live. I am your host, Krista Porter, here at the Nebraska Library Commission. Encompass Live is the commission's weekly webinar series where we cover a variety of topics that may be of interest to libraries. We broadcast the show live every Wednesday morning at 10 a.m. Central Time, but if you are unable to join us on Wednesdays, that's fine. We do record the show as we are doing right now, and it will be available for you to watch later at your convenience. And I'll show you at the end of today's show where you can access all of our show recordings. Both the live show and the recordings are free and open to anyone to watch, so please share with your friends, family, neighbors, colleagues, anyone you think might be interested in any of the topics we have on Encompass Live. For any of you who are not from Nebraska, the Nebraska Library Commission is the state agency for libraries, so we'll be similar to your state library, possibly. So we provide services and programming and resources and grants to all types of libraries in the state, so you will find all types of shows on Encompass Live. Shows for all types of libraries, yeah. Public, academic, K-12, corrections, museums, archives, the list goes on and on. Really our only criteria is something to do with libraries. Cool libraries are doing, something you think they could do, be doing. We're the Nebraska Library Commission staff that sometimes do presentations about programs and resources and things we're offering through the commission or here in Nebraska. But we also bring on guest speakers, and that's what we have this morning. With us today is Marcella Fredtrickson. Good morning, Marcella. Good morning. And she is from the University of North Carolina, Wilmington. And this is a session that I saw that she did at Computers and Libraries, the Computers and Libraries Conference, beginning of this year, yeah. We're already past internet librarians, I get a little... And I reached out to her to invite her to come on and share what her presentation and the work she's done on this with all of us here today. So I will just hand it over to you, Marcella, to take away and tell us all about your presentation on racial and gender bias in search. Great. Thank you so much. Thank you for joining me today for this presentation on racial and gender bias in search. As Krista stated, I gave this presentation at the Computers and Libraries Conference in March 2023, and I also gave it a few other times for ASERL and NCLive in 2023 and 2022. I would like to open this session by taking a moment to acknowledge my privilege as a white cissexual person. The topics we will be discussing today may be difficult, and I will try my best to respect and give space to the complexities around racial and gender bias. So in today's presentation, we'll go over some basic definitions, examine the idea of search neutrality, explore racial and gender biases in search and library discovery, and try to understand how human beings influence search algorithms to produce bias results. To start our definitions, let's first look at bias. Here's a screenshot showing the Oxford language's definition of bias in Google search results. Bias is defined as prejudice in favor or against one thing, person or group, compared with another, usually in a way considered to be unfair. So bias can be understood as a preference for or a prejudice against something that is often unfair or unjust. There are many different types of bias, such as unconscious or explicit bias, sorry, as conscious or explicit bias, unconscious or implicit bias, cognitive bias, prejudice or stereotyping, prejudging, and so on. Today we're going to be seeing examples of how bias can serve as some technology. Some of these biases we'll examine today are explicitly racist or sexist. Some work on inference or suggestion and rely on known stereotypes. As Matthew Reidsma states in his book, Masked by Trust, quote, when supposedly objective search results reflect the kinds of structural inequalities that we struggle with in our daily lives, like racism and sexism, there is a good chance that these systemic biases have crept into the algorithms behind search. We also want to understand what algorithms and mathematical models are. We often hear the terms algorithm or model when discussing search, machine learning, or artificial intelligence, but understanding what they really are can be hard to pin down. Miriam Webster's broad definition of an algorithm is a quote, step-by-step procedure for solving a problem or accomplishing some end. We can think of a recipe as an algorithm or a knitting pattern as an algorithm, but algorithms are also much more complex than this, and their definitions can change depending on who is defining the term. The algorithms we encounter in our daily lives, and there are many, are really algorithms upon algorithms, all connected together to form complex structures powering things like Google search. And we have very little understanding of these algorithms. Reidsma describes commercial algorithms like the one Google uses as quote, black boxes. The components are working of the systems are hidden from view. Search algorithms, he says, are largely kept secret because they are the primary intellectual property asset of the parent company. And so sharing the details of how they work would devalue the primary revenue generating asset the company has. The Encyclopedia Britannica defines a mathematical model as quote, either a physical representation of mathematical concepts or a mathematical representation of reality. In the book weapons of math destruction, Kathy O'Neill writes that a mathematical model is quote, nothing more than an abstract representation of some process. And that quote, the model takes what we know and uses it to predict responses in various situations. But she also pushes back on the idea that a mathematical model by its very nature is exact or impartial models make mistakes because there are simplifications. No model, she says, can include all of the rules complexity or the nuance of human communication. Some of the characteristics that turn a model into a WMD or weapon of math destruction include opacity or invisibleness, pernicious feedback loops that make the model more unfair over time and the large scale scale of some models, such as those used in HR processes, health administration and banking. Mathematical models are formed by inputting data into algorithms. Algorithms are procedures we follow while models are built using algorithms. But algorithms are not inherently correct, as Sarah Wachter Betcher writes in her book, technically wrong. She writes quote, algorithms are just a series of steps or rules applied to a set of data designed to reach an outcome. The question we need to ask are, who has decided what the desired outcome was? Where did the data come from? How did they find good or fair results? And how might that definition leave people behind? Two terms that are becoming much more popular in the media these days are artificial intelligence or AI and machine learning. As we'll see later in the presentation, AI is a new frontier for search engines. This definition is from the Google Cloud product page. And I just want to read the last paragraph. On an operational level for business use, AI is a set of technologies that are primarily based on machine learning and deep learning used for data analytics, predictions and forecasting, object categorization, natural language processing, recommendations, intelligence data receivable, retrieval and more. Here we have another definition from the Oxford languages on Google, this time for machine learning. Machine learning is defined as the use and development of computer systems that are able to learn and adapt without following explicit instructions. O'Neill says that with machine learning, a quote, fast growing domain of artificial intelligence, the computer dives into the data following only basic instructions. The algorithm finds patterns on its own and then through time connects them with outcomes. In a sense, it learns. So now that we have a shared vocabulary, let's look at the concept of search neutrality or objectivity in search. What do we mean when we say search? From January 2022 to January 2023, Google dominated the market at 92.9%. The term Google it has become ubiquitous for searching the internet. So really, when we talk about search, we are most likely talking about Google. Reason states that Google handles around 3.5 billion searches a day from over 4 billion internet users. And of these searches, Google says 15% are ones they have never seen before. In its own words, Google says its mission is to, quote, organize the world's information and make it universally accessible and useful. They also share that they earn most of their money by showing ads in search results on Google.com. In the book Algorithms of Oppression, Sophia Umogen Noble makes a strong argument that Google is not an information company, but an advertising company. Beholding to a shareholders and to the bottom line. Noble says, quote, search happens in a highly commercial environment and a variety of processes shape what can be found. These results are often normalized as believable and often presented as factual. And on its about pages, Google is very careful to describe itself as either an information provider or an advertising company. Risma says that this quote allows it and other companies to avoid regulations that apply to existing business sectors. As Google tells it, it sells ads, but only to better serve results in search, which are determined not by a human but by complex algorithmic processes. These next few slides feature a lot of screenshots from Google's documentation called how search works from both April 2022 and more recently. In both examples, Google explains it's advertising as a way to enable freely accessible search for all people. In April 2022, Google explained that ads are displayed only when relevant to the task or the user's search query and that their interest is in quote showing only useful ads. Today, this text has been updated and streamlined even more. In 2023, Google now clearly states in unequivocal language, quote, no one can buy better placement in the search results. Google also clearly highlights that ads are labeled and easily distinguishable from the rest of the page. They reiterate in multiple places that no one gets special treatment or advantages in search results. Google says their results are the most relevant and reliable. Ritzmer describes the authority of algorithms to make everyday decisions as quote, or increasing faith that the algorithm will deliver a more objective decision than a human could. That somehow the algorithm eliminates the human biases that we often see coloring our decisions. And Google's descriptions of how their algorithms work reflect this idea. As Ritzmer states, the company actively reminds us that its tools are making choices and selections through algorithms rather than through human curation or judgment. Google is very careful to point out that real people are involved in measuring search results but not in ranking. Ranking is purely algorithmic and therefore, as we've seen suggested earlier, not subject to human bias. So how does Google rank and serve relevant results? The company says that Google's ranking system sort through hundreds of billions of web pages and other content to present the most relevant, useful results in a fraction of a second. Search algorithms look at many factors and signals, including the words of your query, relevance and usability of pages, expertise of sources, and your locations and settings. But Google is aware that problematic content can appear. In April 2022, they said quote, no system is 100% perfect and quote, occasionally results may contain content that some find objective or offensive. Note that Google does not say that they serve results which may include bias or even factually incorrect information, just that some results may be objectionable or offensive. And they are quick to point out that quote, such problematic content does not reflect Google's own opinion. So excuse me. They also clarify that policy violating content is resolved by improving automated systems first. And then in some cases, human beings may take manual action to block the content but not rearrange ranked results. Google is careful to distance itself from problematic content while also emphasizing the objectivity of automated systems. Google does have content policies for search results which include child sexual abuse, highly personal information, spam, site owner requests and valid legal requests. They also have additional policies around their search features, such as panels and carousels, which they acknowledge may be perceived to have quote, higher credibility because of how they are presented. These policies include harassing content and hateful content, but only in search features and not in web results. Other search features such as autocomplete and dictionary boxes also have specific policies. Looking at autocomplete or auto suggest systems specifically, we see the same pattern. Google distits itself from the problem by saying quote, autocomplete predictions are perfect and quote, predictions aren't assertions of facts or opinions. And then once again, they emphasize this system as the solution. But they also fall back on enforcement teams that manually remove predictive content that violates Google's policies. In April 2022, autocomplete had policies against election-related predictions, health-related predictions and sensitive and disparaging terms associated with named individuals. By February 2023, Google expanded their autocomplete policy section to include references to their overall content policies and policies for search features, as well as adding the autocomplete specific policy against quote, serious malevolent acts. Many of these policy changes can happen at any time without warning or notification. In this documentation, we can see how Google reinforces the idea of search neutrality by emphasizing the automated processes behind search. Algorithms and not human beings serve search results that are most relevant to the user, which leads us to the next portion of my presentation, what we believe about search, and how we use it. I'm just going to jump in quickly, let everyone know that, as I didn't mention at the beginning, the slides that Michelle has here, they're going to be available afterwards. She's already sent them to me, so please don't try to write all this stuff down. We will have all of these slides available to you afterwards to go along with the recording. If you have any questions or comments or thoughts about the presentation, type them into the questions section of the GoToWebinar interface, and I'll gather all those up and we will answer them at the end after the presentation is complete. Please do post your questions whenever you think of it, so you don't forget. Thank you. Okay, so this data is from the 2012 Pew Internet and American Life Survey. In 2002, 52% of all Americans used search engines. By 2012, 73% of all Americans used search engines. And 91% of search engine users said they always or most of the time find the information they are seeking when they use a search engine. 73% of search engine users say that most or all the information they find as they use search engines is accurate and trustworthy. And 66% of search engine users say search engines are a fair and unbiased source of information. The simple design interface of a Google search is purposeful. Reason states that the interface works to enhance the user's trust in the search engine. And he points out that the design choices Google has made have been copied by other search engines, library discovery systems, and library research databases. Such as shown here on this screenshot of UNCW's Summon page. In critiquing these design choices, Noble writes that when designers use clean aesthetics to cover over a complex reality, to take something human, nuanced, and rife with potential for bias and flatten it behind a seamless interface, they're not really making it easier for you. They're just hiding the flaws in their model and hoping you won't ask too many difficult questions. The Pew Internet and American Life Survey from 2012 also shed some light on how we use search engines. We are confident in our search abilities. More than half of search engine users or 56% say they are very confident in their search abilities, while only 6% say they are not too or not at all confident. And the vast majority of search users report being able to find what they are looking for always at 29% or most of the time at 62%. What do people think about bias in algorithms? From the 2019 Pew article, Seven Things We Learned About Computer Algorithms, they said that more than half or 58% of people surveyed believe computer programs will always reflect the biases of their designers, which brings us to examples of search bias. In her book, Technically Wrong, Sarah Wachter-Betscher examines multiple cases of bias in information technology systems. One such example is of Google Photos autotagging feature mischaracterizing selfies of two black people as gorillas. This terminology of course has a long racist history when used to describe black people. No human being was involved in this tagging, as she explains. The technology is based on deep neural networks, which are massive systems of information that enable machines to see much as the same way the human brain does. Neural networks use a learning algorithm. Instead of following programmed, predetermined steps, the algorithm is fed historical data which it parses, identifies patterns, and then makes determinations about new information based on those patterns. In response to this mistake, Wachter-Betscher reports that Google's Yonatan Zunger, chief social architect at the time, said on Twitter that quote, we're working on longer term fixes around image recognition itself, e.g. better recognition of dark skin faces. Which Wachter-Betscher believes implies that the historical data fed to Google Photos possibly failed to include pictures of black or other minority people. This is a significant lapse in judgment, which Wachter-Betscher says is based on quote, assumptions that technologists so often make that the data they have is neutral and that anything at the edges can be written off. By 2022, Google was actually promoting a feature of its Pixel 6 phones called Realtone, which represents quote, nuances of different skin tones for all people, beautifully and authentically. From this, we might assume that they solved their image recognition problem and even decided to promote its solution as a feature advertised to consumers. However, a recent article in the New York Times points out that in Google Photos and in Apple, Amazon, and Microsoft Photo Products, searching for terms like Gorilla and other primates is difficult. In Google Photos, a search for Gorilla will produce no results. Eight years have passed since the tagging incident in Google Photos and yet the company has still not corrected or improved its systems. Perhaps Google is more concerned with the optics of getting a search wrong and possibly exposing bias in its systems again than with providing a program that accurately tags and retrieves information. This next example doesn't involve Google, but it may be interesting to other academic librarians. O'Neill's book, Weapons of Math Destruction, takes a look at how mathematical models, unregulated and never challenged, reinforced discrimination in many different aspects of our lives. She takes a hard look at the college rankings from the US News and World Report. In order to quantify something as nebulous as, quote, educational excellence, US News built a model based on proxies in 15 areas or categories that seem to correlate with student success, such as SAT scores and acceptance rates. Then, as the ranking reports grew in prominence, a feedback loop developed that became self-reinforcing. As O'Neill states, if a college fared badly in US News, its reputation would suffer and conditions would deteriorate. The ranking, in short, was destiny. The larger the scale of the rankings reports, the more damage they do. The problem with building a model based on proxies is that it is easier and simpler to gain the system. Schools begin to invest time, effort and money into the 15 areas ranked by US News. But one of the areas never accounted for in the proxies was tuition and fees. O'Neill states that between 1985 and 2013, quote, the cost of higher education rose by more than 500%, nearly four times the rate of inflation. And just as the rankings reports have grown to include law schools, business schools, and even high schools, SOTU has an entire industry of consultants, coaches, and courses that promise to get those who can afford it into elite schools. O'Neill says, quote, the result is an education system that favors the privileged. It tilts against needy students, leaving out the great majority of them and pushing them down a path toward poverty. It deepens the social divide. And as we've seen in recent years, questions about college rankings and the lengths on people will go to gain the system have abounded in the news. Ruha Benjamin coins a new phrase in her examination of bias in technology, the new gym code. She defines this as, quote, the employment of new technologies that reflect and reproduce existing inequities that are promoted and perceived as more objective or progressive than the discriminatory systems of a previous era. She goes on to say, quote, the animating force of the new gym code is that tech designers encode judgments into technical systems but claim that the racist results of their designs are entirely exterior to the encoding process. One example she gives is of Google Maps telling her driver to turn right on Malcolm 10 Boulevard. Google Maps had recognized the X in Malcolm X's name as the Roman numeral 10. Benjamin argues that this supposed glitch is really an example of the cultural norms of programmers being coded into technical systems, a kind of digital gentrification marrying what is happening in urban areas across the US. She says, quote, this is more than a glitch. It is a form of exclusion and subordination built into the ways in which priorities are established and solutions defined in the tech industry. Glitches are not spurious, but rather a kind of signal of how the system operates. Another example of racial bias in search is from 2016 when searching the phrase, three black teenagers produced dramatically different results from three white teenagers or three Asian teenagers.  Searching for three black teenagers today brings up articles, images and videos about the racist search results from 2016. As Benjamin points out, Google is, quote, able to optimize online content in ways that mitigate bias. The technical capacity was always there, but social awareness and incentives to ensure fair representation online were lacking. Noble's book algorithms of oppression contains many examples of bias search results and autocomplete predictions. One example she raised is the search results for the word Jew prior to 2005, which included a significant number of anti-Semitic pages. In 2005, Google published, quote, an explanation of our search results, explaining that anti-Semitic results for the word Jew rely on computer algorithms, and even though they were problematic, they were computer generated and thus not the company's fault. Google then recommended that users search using the terms Jews, Judaism or Jewish people, putting the responsibility of finding factual information on the user. Noble asserts that this statement is, quote, claiming that the company can do nothing about the words co-optation by white supremacist groups. She replicated the search in 2012 and noticed that Google then linked the explanation from the beige box at the bottom of the page. However, a search today for the word Jew produces no problematic results and no links to the explanation page, again reminding us that Google can and does influence search results, whether through automated systems or human beings blocking specific kinds of content. This screenshot from 2022 shows the first half of results for the word Jew in Google search results. No anti-Semitic resources appear at all on the first page of search results. In Germany and France, where it is illegal to sell Nazi memorabilia, Google filtered their search results to block such types of sites from appearing as far back as 2002. As Noble summarizes, quote, what these cases point to is that search results are deeply contextual and easily manipulated rather than objective, consistent, and transparent. She clearly assigns blame saying racist and sexist web results are in search are created by, quote, a corporate logic of either willful neglect or a profit imperative that makes money from racism and sexism. It's harder today to find examples of biased search results and not to the magnitude that Noble found when researching algorithms of repression.  Since then, Google has obviously removed some of the most egregious racist and sexist search results as you cannot replicate Noble's searches. But stereotypes and biases still exist. These are screenshots from March 2022. Bias doesn't stop just because Google enforces certain policies on certain search results. As our society grows and changes, so too does the biases, whether explicit or implicit, that accompany the human experience. As we learn, so too do the algorithms and models that influence so much of our lives. There are also troubling reports of racial and gender bias in artificial intelligence systems and its products. According to this article from NIST, bias can manifest in the AI systems and the data used to train them. But like some of the examples we've examined today, bias is also reflected in how AI systems are used in our society. Benjamin points out that machine learning systems, quote, allow officials to outsource decisions that are or should be the purview of democratic oversight. Public agencies employ systems built by private companies who face none of the checks and balances that a public official or agency would face. NPR reported in March of this year that the Bing AI chatbot launched by Microsoft called a reporter, quote, ugly, short, overweight, unathletic, and then went on to compare him to Hitler, Polpa, and Stalin. AI Power Search may be the next frontier in which we have to uncover and confront racial and gender bias. As Benjamin states, quote, with emerging technologies, we might assume that racial bias will be more scientifically rooted out, yet rather than challenging or overcoming the cycles of inequity. Technical fixes too often reinforce and even deepen the status quo. So now I would like to turn our attention to examples of bias in library discovery systems. Matthew Reeds-Mudd published his book, Masked by Trust, in 2019. He examines bias in library discovery by querying various discovery platforms and recording his results. In summary, he found that a year and a half after the election, the topic explorer showed outdated Wikipedia entries for Donald Trump and Barack Obama, declaring Obama the president and Trump a real estate and entertainment model. In Ed's Go Discovery service, a search for, quote, rape culture showed an encyclopedia article about rape myths in the research starter at the top of the page. In summing, a search for, quote, Muslim terrorist in the United States resulted in a Wikipedia entry about the Islamic religion in the U.S. as a whole. Reeds-Mudd also found very different search term results for searching the terms 9-11, September 11, and September 11, with the 9-11 search appearing to emphasize conspiracy theories about the event.  Reeds-Mudd points out that library users are, quote, searching for big, challenging, often contentious topics. There is no mathematically correct answer to a question about abortion rights or the death penalty, yet libraries and vendors have promoted library discovery tools as effective guides through difficult subjects. And that brings us to the end of my presentation. Reeds-Mudd says that the unexpected bias results that appear in seemingly objective search tools are the result of treating everything like a math problem, assigning numerical values to unquantifiable things, of accepting measurable proxies for slippery concepts and ideas. I believe that it's important to start with knowledge. If we know algorithms and mathematical models are biased, and sometimes even untrustworthy, we can work towards a solution. We must recognize the limitations of algorithms and learn how to approach search results with skepticism. And we must share this knowledge with our patrons and users. It's not just a matter of improving diversity in Silicon Valley. In October 2021, CNET reported that multiple tech companies had signed on to a DEI initiative to improve diversity and inclusion in their companies. But as Noble points out, this puts the onus on marginalized people to learn coding or become programmers or somehow step into leadership roles. She says, quote, we have automated human decision-making and then disavowed our responsibility for it. Algorithmic bias is a multi-layered problem that needs many different solutions working together. Wachter Betcher writes, quote, if technology has the power to connect the world as technologists so often proclaim, then it also has the power to make the world a more inclusive place simply by building interfaces that reflect all its users. So here are some films and books to look for if you're interested in more information on this topic. I was not able to discuss all of these resources, most notably the film-coded bias, but I do recommend them. And for questions, here is my contact information. And I will stop sharing my screen. Actually, you want to put the screen back up there to show your contact info for a bit while we're at it. There we go. In case anybody has any questions about anything that you did have in any previous slides or if we need to pop back to anything, we'll have that available. Thank you. Thank you so much, Marcel. This was, as you said, I appreciated your, if I call it a warning, disclaimer at the beginning that this is, I mean, I think it's pretty obvious with the title of your presentation, very serious discussion, things to talk about. Yeah. You're not going to be a happy-go-lucky kind of, just to thank you, thank you for thinking about here, because there's things that are very important and serious going on in the world that we need to be presenting properly to whoever's using our libraries, public libraries, academic libraries, etc. So I really appreciate you bringing all this information together, which is why I wanted to have you come on here today because there is a lot going on out there, but it's great to have someone to bring it all and say, okay, here's all the things you need to be thinking about and be aware of. I don't know if we have solutions for everything, but awareness is the first step and an importance. Absolutely. So if anyone has any questions, nobody typed anything while you were presenting, that's fine. If anyone's listening, I assume very intently. If anyone has any questions, comments, any of your thoughts about what's going on with this, go ahead and type into the question section. I was at the very beginning when you were talking, yeah, the math book there, I was like, I was, I was surprised that math would be part of this presentation. I was like, I didn't know where it was going to be, math was going to be required here. But I get it. It's, and that's, as you said right here at the end, that's the problem. You can't mindlessly let another algorithm do everything. It all needs to be examined. You can't, I mean, everyone in the whole AI thing, oh, that just makes me nervous, just in general, with everything with it. Yes, it has its uses, but there's so many things that are not good or comfortable about it and you just can't let it just do its own thing. No, we do, we have to challenge it. We have to watch it. We have to remark on it and record what we find and share that information with others, definitely. I've started some research into generative AI and bias and I'm already seeing results that show clear, stereotypical, implicit, unconscious bias. There's the cliche with voice, people have always said about technology and computers, and now it applies to AI, garbage in, garbage out, which is very, that doesn't cover everything, but yet it only knows what you've taught it and told it. Now, ideally, AI is supposed to learn to somehow, but it can only learn based on what we human beings have. Right, exactly. It's not going to come up with its own original thoughts or ideas. So, yeah, and we need to be very, very much aware of that. And some of the things, and I think the whole Malcolm X, Malcolm 10 thing, I mean, that's just, I mean, I don't want to, you know, make light of it, but that seems just laziness. Everyone knows Malcolm X. That should be something that some programmer, someone should have immediately said, here's something we need to fix and make sure it only knows that when this phrase is there, this is what it is. It's not like it's some obscure unknown thing. Right. They programmed it just smart enough to recognize X as a Roman numeral, but not smart enough to recognize the phrase Malcolm X. And that you need to say it in a different way. Right, exactly. Seems like such a simple thing. It's just like another thing just makes you go, okay. And some of the solutions you mentioned, I think, you know, you got into the end there about the databases that we use. Is there any recourse for us, since someone's asking, as the librarians who don't have a lot of choice in what, you know, let's go databases we have is there anything we can do from our side of it to fix these issues within the system? Is there anything that we can do we reach out to the database providers? Is there something in our settings? And I know it's probably, you know, be specific to each system, but what would be someone who wants to really jump on this and start, you know, telling nebsco, you need to fix this, because exactly. Riesma advocates very strongly for librarians to do the work, to do the research, to run searches, look for biases, take screenshots, contact your vendors, you know, stay on top of it. And I think librarians are especially well versed or well trained in being able to do that kind of research, because we know about search and retrieval, we know how to analyze sources and see patterns and, you know, report, make reports on them. So I think, you know, in some ways, it's between a rock and a hard place, because you have the vendors you have, and changing them can be very difficult and very expensive, but you also have the ability to draw attention to these things when they happen. You can report them to listservs, you can report them back to the vendor, you can put them on blogs and Twitter and such stuff like that.  Let's try and do something about it to hopefully get that fixed. But also, if you have any people in your library doing these kind of searches, you need to teach them, train them in, you know, that critical thinking skills, that's something that's a critical thinking. Yes, exactly. Teaching not just how to use the database, but then don't just trust it blindly, use your credit. Right, exactly. Sometimes it's a whole other whole class and training on its own. Yeah. College and universities, how do, how do I evaluate what I've come up with?  I helped her do a search on something in our online catalog. It's okay, here's the search results. Now you got to figure out which one is the best for your particular research that you're doing. And she said, I'll just take the first one. No, no, no, no. That's not how this works. No, you've got to look, no, I'm just taking the first one. Thanks, bye. Like, oh, wow, you're not going to do very well. I did my part, but I was like, oh, no, no, no. You've got to dig deeper, you know, because really the fourth book in your search results was really about your topic. Right, exactly. Exactly. I think librarians that can advocate for information literacy in their libraries, courses, seminars, webinars, you know, I think that also is another really great way to bring this topic up. Because you go and find the click here to get this kind of search into these kind of filters into, okay, now you have your search results, then what? Then what, right? Absolutely. Well, I believe that most of anyone has any other questions, still got a little bit of time. If you want to ask anything or share anything about the topic or anything you've done at your library that you've had a success or failure in this with, we can all learn from failures too. That's something to be aware of too. It's okay to, you know, struggle with those things like I did a long, long time ago. But I think this is definitely something that, like I said, we try to teach people to pay attention what they're doing. And hopefully this is something that, I know everyone is aware that this is an issue, but it's something that a lot of people are dealing with, racial and gender bias in life in general. And it's something that as librarians, I think we do very good at being supportive and helping that everyone gets the right information out there and doesn't get misinformation and bad information and looks deeper into what they're looking at and not just, yeah. Yeah. That was interesting that the 9-11-1 really, I didn't realize, when you start talking about it, I was like, oh, that's weird. And then when you said 9-11 comes up with more conspiracy things, oh yeah, I get it.  Yeah. Those kind of things you learn. Yeah. Right. And then, you know, to know that someone or Edsko hasn't done the work to normalize those terms so that you get the same information for each search, you know, is kind of shocking, is kind of, and disappointing. Like, yeah. And that's like, I mean, librarian, catalogers, that's their, their like main goal in life. Make sure every, no matter what someone types in, they'll find the right resource and it's going to take manipulating the metadata behind there to make that work correctly. And that's why catalogers are, I think the saviors of our libraries are at least, you know, the heart of the library, because if you can't find what you're right, then you're looking for, and it takes someone going in and manipulating the data and saying, well, you have to make sure if someone types in this, this, this, this, or this, they're also going to all come back to this results, the same results, no matter what happens. And you think database companies that have been working with libraries for however long they've existed, they should know this. Yes. Yeah. They should know this. Going back to that sad, sad laziness, which just seems very flipping, flipping, but I don't know. Sure. Yeah. All right. It doesn't look like we have any other questions or comments coming in. I can't see if people are typing to wait for the message to pop up. But I was going to, I think I'll start working towards wrapping things up for today. Thank you so much, Marcella, for being here with us this morning and talking about this very difficult topic and important, I think. And hopefully that this will get the word out to more people about what we can do and that it exists and something to just be aware of when people are using your library's resources and do what you can to correct this. Yes. Yeah. Absolutely. Thank you for having me. I really appreciate your time this morning. This is a great presentation for us. Thank you so much. All right. I am going to pull presenter control to my screen here now. So I'll get there we go. There we go. All right. We do have some thank yous coming in from people in the chat though. Thank you for everything you share. This is great. Yeah. All right. So here's the session page for today's show. As I said, we are recording this as well. Yeah, I do have the slides so they'll be available. And I'm going to pop over to our main Encompass Live page here. Excuse me. If you use, talking about search engine, but it works.  Nobody else is allowed to use that name. So you will find a link to our main page and link to our archive page. If you use whatever search engine you like to use, these are upcoming shows for the rest of the year and into 2024. But our archives are right here. I said I would show you this. If you click on there, most recent ones at the top of the list. This is last week's show. Today's will be here. It should be by the end of the day tomorrow at the latest. And you'll have a link to let's see this one. It'll be just like this one from last week. A link to the recording, which is on our YouTube channel, the Nebraska Library Commission is a YouTube channel where all of our archives go. And a link to the slide presentation. Everyone who attended today's show and registered for today's show get an email to me as I'm sending it out to our mailing list here. But you also get that directly to let you know that it's available. We also push it out onto our mailing list here in the Library Commission and our social media. We do have a Facebook page for Encompass Live. It's linked off of the show page. If you'd like to use Facebook, give us a like over there. And we do reminders. These are reminders to log in to today's show, presenter posts. And then when recordings are available, we post here as well. So if you do like to, if you use Facebook, give us a like over there. We also use the hashtag Encompass Live, a little abbreviation for our show name in Twitter and Instagram to post out reminders about the show. So you can follow us there as well. While I'm here on the archive page, I will just show you, you can search our archives, our show archives, see if we've ever had a topic on anything you might be interested in. You can search the full show archives or just limit it to the most recent 12 months if you want something just very current. That is because this is our full show archives. And I'm not going to scroll all the way down because as you can see as I'm scrolling this huge page, this goes back to when Encompass Live first premiered, which was in January 2009. So we're in our 15th year, oh my gosh, of the show. So, but we have all of our show archives going back to the beginning. So do pay attention to the original broadcast date of any recording you watch. They all have the date there so you know when it happens, when it was first done. Some of the shows will be great and fine and stand the test of time. Some things will become old and outdated. Resources may have changed drastically or disappeared. People probably work at a completely different library than when they did possibly work in a completely different place than when we started. So just pay attention to that original broadcast date. But as what libraries oftentimes do, we do keep things for historical purposes so that they can always be available. And as long as we have some place to post these, which right now is our YouTube channel, we'll always have our show archives out there for anyone to watch going back to the beginning. All right, so that does wrap it up for today's show. I don't see any other last minute desperate questions or comments anyone typed in. So I think I will say, I will finally wrap things up. Thank you everybody for being here. Thank you so much, Marcello. It's good to have you with us this morning. And maybe we'll have you all on a future episode of Encompass Live. Oh, that would be great. And so as I said, here's our upcoming shows. And next week, our topic is redesigning a library website. So if you do have a website that might need a little work, or if you're thinking about doing something, this is a session that may be good for you, another University of South Carolina presenter. And we have all other shows coming up as well. So please do sign up for that show and any of our future episodes. So thank you, everybody. Thank you, Marcella. And hope to see you all on a future episode of Encompass Live. Bye.


I think they're ranked maybe top 10 in punt return and kickoff return. What are they doing well? Well, Jen, they're doing about everything well. I think they're ranked in the top 10 in almost all major categories right now. Punt return-wise, they got a really explosive returner in Harris, number 11, who's obviously a dual guy. It's fast, quick, explosive, gets the top speed quickly, and he can make guys miss in space. So we've got our hands full, but they also have a really good core group that do a great job of blocking for them. So we'll have to go down there and play our best game and try to figure out what we need to do to contain Harris and kind of go from there because they're explosive and they're really good. How much you take it as when you get a touchdown like you did near the goal line that was set up by baby Brett's punt, how much did you guys take pride in that? How much did Brett take pride in that? Yeah, definitely. That's the one thing Coach Rabel brought up in the meeting about playing complementary football. And we try to do that each and every single week of helping out our team win. And when we get a chance to go and pin an opponent down inside the 10-5 yard line like we did this past week against the Rams, it gives the guys confidence and it gives a little bit of energy to our team. And then obviously the defense stepped up, got a pick, and then offensively we went down there and scored. So we take pride in that and obviously Brett has been doing that for a very long time here. So that's always good that we can help the team out in certain aspects. Talk about the core guys. He brought Zubner back to a late camp. Why was it important to get him back in here? What does he add to your team? Yeah, he's a leader. He's been doing it for quite some time and he can help out the rookies talking to them because he came in the league. No one really knew about him. Really kind of set the tone for him being a really good special teams player early on in his career. So that part really helps out the young players to see a guy who comes in, works really hard, and then obviously continues to stay in the game because he does really well on special teams. So we were happy to get Nick back and we're hoping he can continue to play well for us. Mike mentioned yesterday that Dez Fitzpatrick has been doing pretty well on special teams on the scout teams as well. How important is that if he hopes to take that jump onto the 53 there? Yeah, it's really important. We've been impressed by Dez. For a guy who comes in, gets drafted, and then has got to play on some special teams too. It's important for him to understand that he might be able to get on the field a little bit quicker if he does really well on special teams. And he's continued to put in the work, working really hard, showcasing his skill set for us, and he's obviously being rewarded here a couple of times because he's shown out for us. So really excited about him, and hopefully he continues to have that progress in special teams along with offense too. The significant difference from him in training camp? Did he, I don't know if he played much in college? Yeah, you know what was good. He did play a little bit in college on special teams. I know there's special teams coordinator really well there in Louisville. And when we drafted him, he said, hey, Dez will be able to play some special teams for you at some point in time, whether it's going to be early or late. He felt like he had some skill set to help us out. And I think what happened was early on, Dez is trying to feel his way offensively.  Offensively, I'm getting better. But now, special teams, I got to really start going up and doing well. And I think he kind of clicked for him a little bit. So yeah, we'll continue to work with him and see if he can get better at it. Flippers last week mentioned DIT for him as the option of kick returns. Is there something different that he breaks, what makes you want to involve him with that? Yeah, I think anytime you get a bigger person back there who can break arm tackles and just even having a vision like a running back will have, whether it's hitting certain holes, cutting, doing different things like that, that always brings a different aspect more than a receiver does. Because receivers are going out there running routes and doing things like that. They're really not reading holes and gaps that are coming open. So we'll continue to work with him and try to get him better. The big thing is going to be with him is consistency of catching the ball. If he can consistently catch the ball, obviously we're not really worried about his running skills because he's been doing it for a very long time. So we'll continue to work with him on being consistent, catching the ball, and see what he can do. How much in the Denver Dallas game, I guess, over the weekend, you see a very kind of odd play that doesn't happen a lot? When something like that happens, do you show the film and use his reminder of, hey, these are the rules because it doesn't happen a lot? Did you sit in our meeting on Wednesday? Yeah. We try to do that each and every week. That's one of the things Matt and I do is we'll watch every single game that went and happened around the NFL. And we'll bring up certain situations to the players. For instance, the Denver and the Dallas block punt and just kind of remind them of what we went over during training camp off season. Here's the rules. Here's what can happen. So we usually try to bring those plays to the players Wednesday, Thursday, Friday, and even Saturday to their attention. But yeah, it was interesting. We end up showing that play on Wednesday and just kind of talked about it again with them of why it's so key for them to understand, did it cross the line of scrimmage? Did it not? So it was really a good refresher for the players to get that.


Oil-accumulating microalgae have the potential to enable large-scale biodiesel production without competing for arable land, or biodiverse natural landscapes, but require induction of lipid biosynthesis through environmental stresses and genetic strategies to stimulate lipid production. This article was authored by P.R.M. Shink, Kalpesh K. Sharma, and Holger Schumann.


for my lines as they tried to find some good consistency for Mario Palma between the sticks for this evening the captain Harding and left back beside him Romain Brackenridge and Gianni Talbot and Akima Jones in the middle of the park got Stuart Cassimprisley and of course Ajuma Johnson and up top a late change with Courtney Allen being brought into the starting lineup Andre Fagan and Shaquille Bradford completing the trio up top yeah 4-3-3 as well exciting win back in Akima Jones. As we take a look at the full-time highlights here at Sabina Park and half of you with the early attempt from outside the box Peter Harrison was behind it and then Dennis was tripped just inside the box was appealing for the penalty got it and Jeremy Nelson converting from 12 yards one nail to Maline's United no issues there for Nelson on the spot half of you still fighting still looking for an opening and that one was dragged wild by Ajuma Johnson Allen busting his way through and taking a shot from distance Harrison holding on at the second attempt Jones's ball inside well ended up taking a shot on target Harrison read that well and then Harrison again there to repel that attack then Jones is born inside at the back post first time attempt from Allen couldn't guide it goal words Allen again in the thick of things and then this one was fired hard by Fagan Harrison with a good save but then an even better one got sued with the header and Harrison again flying to his left getting hands on it and then Maline's United went two goals clear Hardy doing well the overlapping run by Jason Wright and the finish beautiful that's his second of the season really beautiful football played by Maline's United today and surely they thought they were on the way to three points over half of you for the first time ever then the game just got absolutely crazy Allen deflected inside and bundled over the line by Shaquille Bradford yep no issues there Harrison almost pulled off another miracle save it's not the first time you would collide with a player with Bradford we'll see that a little bit later on but then a second yellow card for this challenge our feeling was that he got some ball a lot of the ball in the fact maybe the yellow card the second yellow card was a bit harsh so he was sent off here's the collision I was telling you about bang penalty awarded to Bradford and pop of you Bradford cracking the crossbar in the process went on the line and out and of course interfering with play coming from an outside position but look at this as they speak about madness Frankson was in line to get clear of the game in my opinion and just a rush of blood to the head and here to win it or to get back into it and Bradford did lose ball came to him and he always seems to be in the right position on March placing that to the right of the keeper good finish two two and there was still time for both teams to get in it but in the end it ended at two apiece 12 shots from a line tonight six on target how about you 15 shots nine and target a very physical game with 26 balls committed four yellow cards a couple of red cards to two more lines and I did my lines and I did had more corner kicks for three and the hub of view with the majority of the possession in the end at 56 percent well Shaquille you got a bit of everything in that game tough game hard fought two goals to go is it your tally is up to five how do you feel about your performance well I'm a static about the performance but still kind of killing myself for the penalty you know who knows what could happen after I scored that one but could have been it's just a part of the game I saw you shaking your head at the end of the game when you're sitting on the turf where you're reflecting over the whole game why were you shaking your head I mean it's one of those game where we will show more potential and create a lot of chances but you know more more finishing work got to be done on the training ground yeah based on the starting formation at 4 3 3 a lot of the time you had to do the work on your own how difficult was that or is it something that you're used to well that's a part of my game from from what I was in no pressing team and I bring the display to all of you so it's not a problem to me all right Shaquille I know a lot of blows today I know you want some rest so go ahead enjoy your two goals congrats on your performance and unfortunately you couldn't get the three points but nevertheless good performance Shaquille Bradford their play of the game the harbour view striker and scoring it


Yeah. So here's a game and I did a little bit of research on this game. So it was printed a long time ago. What year 2004? That's a long time ago. And it was called the box art looks like it's like an early nineties game. I mean, look at that box. It looks like an even older game. But the game was called gen sites fun Theven. Does gen sites mean archaeologists or something like that? Archaeological sites. I don't know. Something like that. And archaeological von Peterprins. Right. So anyway, so for two to four spieler. So the game was reprinted in a much better version in what year? Oh, sorry. For ages 10 and up trying to read this old German rule. Yeah, we don't care about that. It was reprinted in 2007. It's just thieves. So I think it's hard to hard to get an ad of print, maybe, but maybe it's not hard to get. I don't know. But we were able to, we had a friend at a copy. We played it a bunch. It's a game I had seen around before at conventions and in game libraries and never had actually played. So we played it. We played it like two or three times. Well, Scott played it and then he comes over to me later because I was doing something else.  And then like the next day I played the digging game and we dug. So the thing about this game is that it's just so thematically good. Right. The theme of the game is this.  Yeah, fake archaeologists.  They're just Indiana Jones. Right. So you start in Yerp and you're going around Yerp researching things. Right. And chasing down rumors. Right. Or you're hiring diggers or getting graduate students to like, you know, be to follow you around. And maybe you're buying a blimp to keep on hand or you buy a car, which apparently is super awesome. Yeah. We didn't think was awesome. You didn't think was awesome. Well, I was told it was not awesome. So we'll talk about this later, but we started the game and Scott's like, I trusted someone.  And I'm like, well, I was told the car sucked, but apparently that was not the case. Car probably be pretty great if I bought it in the beginning of the game. And then a car appears. And of course, our friend buys it right away. I was also told the shovel was awesome. So I got a bunch of shovels right away. Not that awesome. Yeah. Shuffle was awesome. But you know what? I only got two shovels. I got three and it still wasn't awesome enough. Anyway, so here's how the game works. You spend time, the game, depending on how many players you have is two or three years long. And everything you do takes time. So you move along the edge of the board whenever you do anything like where the victory point track is on regular German games instead. That's just time. So it's 52 weeks in the year. So you move, you know, if you do something to take six weeks, it's like moving from Moscow to London and then doing some research could take you like one week per space. You have to move, which could be like three spaces or four spaces. Moscow to London is pretty far. Yep. And then you spend time like learning about Mesopotamia. That takes three more weeks. So like a car. So you move your guys seven spaces because it took seven weeks. Yep.  But if you just find some schmuck with a rumor on the street, it only takes like one more week and you go until you run out of time. So everyone is, you know, whoever's turn it is, is the person who has currently spent the least number of weeks. So if you've spent the least amount of time, you spend a little bit of time. And if you pass someone, they now spend a little bit of time and everyone doesn't move whenever they're currently the person who's done the least stuff. Yep. So whoever's furthest back always takes the turn. So sometimes you can do stuff twice in a row.  Now that in and of itself is a novel mechanic. It's so thematic. It's like you're spending time going around doing stuff and everyone has the same amount of time to do stuff as everyone else because you can't end the game by going over or under. You end the game going exactly two or three years of stuff doing, but also as a mechanic, it is very novel because it sort of provides a turn order problem. It provides a lot of opportunities for a clever game designer. There isn't something like, Oh, you got to be the first player and I'm sitting to your left and then not to design a very well balanced or self-balancing game because sure, the shovel is awesome. Shovel takes a lot of time.  And then everyone else takes like three or four turns before you get to go again. Right. Now the other thing you do is you can go down below out of, you can leave Europe. You can go to, well, Greece is still in Europe, but you can go to Greece, Crete, Egypt, Mesopotamia, Palestine. Is there somewhere else to go? I forget. Right. Rome is part of Europe. It's not part of the archaeology dig places.  But when you get there, there's only one thing to do and that is start digging. Now the digging mechanic is the best. Now it's the best, but at the same time it does make the game a little random, but it makes the game a lot random, but it makes it thematically really fun. This game is all about theme. It's not about being a strong competitive game. Right. So I've already come up with some pretty strong strategies. I mean the strong strategies, but in the end it's the mercy of the desert. Right. So let's say you go to Egypt and you've studied Egypt a lot. You've studied for Egypt. Right.  Well, if you dig in Egypt for eight weeks, you get to dig this much dirt in Egypt. And the more you know about Egypt or the more time you spend, the more you get to dig. So the bag that you dig from for each site, each site has its own bag. You literally dig. There's an Egypt bag and you reach your hand in there and pull out a number of tiles equal to how many you're allowed to dig. Right. And you're digging through that bag. And in the bag, it starts the game with half treasure and half dirt. Any treasure you get is treasure you get. And it's worth victory points. Any dirt you get is dirt. Now what makes the game amazing is that when you're done digging the Egypt bag, you take all the treasures and you keep them, you know, you want to hold an exhibition or who knows what you're going to do with them. But all the dirt goes back in. You fill in the holes.  Right. Cause there it is way more dirt than treasure in there now. So they need to dig even better than you dug to have a chance of getting anything. I once dug 12 tiles from a bag that had a reasonable amount of treasure in it and dug 12 dirt. I've also saw some people dig like six tiles out of a bag that was mostly dirt and get like five treasures. So it's like there's so much drama when digging, but it's just like the drama of doing real archaeology where you go to a site and you start digging. It's like, are we going to find anything?   So even further to make to really drive the theme home, it's got all these other little elements like it has a sheet that tells you what all the treasures are.  Now that's the thing about the game is that the theme of it is so good, but the mechanics are only okay. Well, I would say the digging mechanic is what makes you say the mechanics are okay because the rest of the game, I would like to make a more deterministic game that uses the same front half, everything but the digging. It's not just the digging that's random. It's also the cards that are random. Well, it's a third in taxes, randomness, or a web of power randomness. So let's say I go to London and I spend a turn researching Egypt, right? And then it goes around and everyone I spent a lot of time doing that.  It's finally my turn again, right? Now there's four cards available. The only thing you can do in Europe is do one of those four cards pretty much. And I look at those four cards and they're all in Moscow. And even though I might want to do one of them, I'm in London. I just happen to be in London now.    Then the card that gets flipped over next is, I don't know, Paris. Rims already in Paris and it's his turn now. And that card only, you know, so he just picks up that card for a lot less weeks than it would have cost me because, and that ends up, it doesn't seem that big a deal. But if it keeps and it might, you know, over time, the whole course of the game, it should even out between the players. It is also mitigated heavily by the fact that if you find a good card, it pushes you way up on the time track. So whoever fell behind will probably get to do a couple turns in a row and reveal more cards. No, that's not saying is if a good card spawns on you, right? Not only do you get that card without having to spend a lot of time, but now it's your turn again. You can get to do a lot more stuff in a lot less time if the cards turn up your way. Well, that's the danger of taking cards and going ahead. If you take good cards and go ahead, you easily give everyone else way more opportunity. You might not have no choice depending on what cards are available on your turn. So it's not just the digging that's random. It's which cards are there on your turn that is also almost as random as the tiles, but not quite as random to take like one, two or three weeks. Good cards take six. I think it's close to being very optimally balanced. It's close, but it's not so perfect, right? There was, I was watching you make very poor decisions throughout that game. No, I was doing fine. I mean, so yes, there is decisions to be made that matter a lot, but it is also a, it's like a half luck, half, you know, skill game. It's not, it's not like a 80% of the luck to the game. The luck of the card getting. I don't think the luck of the card getting is not nearly as big as the digging luck. I'm not going to be on that. I would say that I'm saying that it's minor. I'm saying it is not huge. It is not major, but it is large enough to be significant. I wouldn't even say it's that significant. I would say it's barely significant. I'd say it is. I think it's a bigger deal in thern and taxes and web of power. It is a bigger deal in those, but it is still a deal. It's a deal. It's not a big deal. Yes. Technically, you're correct. It's a deal. It's a deal, right? So you could lose a game because bad tiles in your turn, but I also think we were playing four player games, right? We've only had two years. If you're playing with less players, which it gives you the third year and also not only more time to do stuff with that third year, right, but also more chances at the cards because less competition. Also, while it's that it becomes much less, it won't be a deal in less players. Well, also, so interesting thing we played. This was like the magfest of playing more like old style classic German games. And what I've noticed is that five players or six players is what everyone's shooting for these days because people have bigger groups. But most of the best German games I can think of and most games that have stood the test of time are four player games. Yeah. We played this with like four or five players. I think we played it with four. It only takes up to four. Okay. I think it's getting at most of the games we played at this magfest. Take four max. I think this is best with two or three. I don't think I think four. I think four stresses the resources of the game too much and it makes the digging much more punishing because now you've got the same amount of competition for the same amount of treasure they don't put in more treasures and more dirt and more player game. It's the same bag, right? Because this game is a race. It is not highly political. So it does not fall into the three player game trap where a three player game is actually a two player game plus a safety monkey. Exactly. So yes, I agree. This game is probably a lot better with three player just because it'll be more fun.  You'll get to do more stuff. Right. It'll just be more enjoyable with more players, less players cut off too short with four players. Yep. It's kind of like how Bonanza is real fun with six and seven, but it's also kind of broken. Bonanza with five is ideal and perfect and one of my favorite games. I think this game is ideal with three, even two. I think this game give me really good. Two would be real interesting. I haven't played it with I think three is the ideal, but I still think two is good. I would prefer two to the four. Board game geek says best with three to four, which is interesting because I think I wonder if there's any special rules for the two because we didn't actually play the two. Yeah. But but the three I think is the sweet spot. There's a big difference between three and four player. I wouldn't say best with three to four. Yeah. I think best with three and then four is kind of like it's playable, but it's not as good as the three. But if you're especially an aspiring game designer, you should play this game for nothing else than to see a game that's so perfectly matches theme. Like yeah, the mechanic theme meshing here is so perfect. Like here's another example. So few games that line it up so well. There is a third another part of the game.  We didn't obviously tell you the rules when you play Puerto Rico and you choose craftsmen. Do you really feel like you're crafting? No. But when you choose to dig and spend six weeks digging in Egypt in this game, you feel like you're spending six weeks digging in Egypt. And when you go around Europe researching, you feel like you're traveling around Europe researching. But there's also these exhibitions. So you'll be digging in Egypt and suddenly you see, oh, there's an expedite and an exposition going on in London right now.  I think it's actually just exhibit in a museum. It's not an expo, but yeah, it's all kinds of, you know, side things that you can do that are kind of interesting and fun to get more victory. And they're all so tight with the theme. Yep. But also the mechanic of you spend time going around a board is a very interesting self-tuning mechanic that you could use. It's a lot of old German games had stuff like that. I recall I started playing a bunch of them again, where even if the game wasn't like super prescriptively designed for balance, they'll have these mechanics where anyone gets too far ahead.  But it's more like the more powerful strategies have an intrinsic game cost that sort of causes them to balance out very nicely, even if you didn't think about it too closely. I think what makes this game such a, you know, such a good thing, if you can find a copy by it, right? Because on the one hand, it is a competitive game, though it's not super perfect, amazing competitive, like I say, a netrunner or something like that, right? But it's competitive enough that someone who likes to play games to win can play this game and think a lot about strategies and make interesting decisions, have a good time. But also because the theme and the mechanic match so well and the fun of reaching in the bag, that person who usually was only willing to play cards against humanity or maybe camel up, camel cup, something like that. It's a fish canonically camel up. Right.  And then you can use this to get both of those people to play the game together and have a good time without that conflict that usually happens. And it's not a lot of games in that sweet spot. You'll see a neat design pattern. So there's a lot of games where like you're like a lumber, games where you can take some number of things. And if you're clever, you can sort of make a combo of the exact values of them. Like power grid is an example, power grid is a game where you can spend exactly all of your money if you're very clever a lot of the time. And it's a huge advantage if you did so. Like if you set the stage for that play to happen later, this game does that in the sense that if you fall way behind in time, you might be able to look at the board and come up with a combo where you get to take two or three turns and then you land directly on top of someone. Exactly. If you land on top of someone on the time thing, top is last place. So you get to take yet another turn. Yep. I feel like a heuristic to know who's doing well is who landed on top of another player the most because that is the person who's doing the power grid thing of spending exactly all of their money more than once in the game. Right. So think about it. You're sitting there looking at the board and you see a card you really want, right? And there's three cards you don't really want so much. So you go and you're in Europe. You're not ready to dig anything yet.  You move two spaces and take it for five turns. Now it's not your turn anymore, but you didn't pay attention. One of those cards you didn't want was on the start spot you spawned and only cost one week to pick up. Even though it wasn't a great card that you really wanted, you could have gotten that card basically for free, but because you weren't paying attention, you didn't get it. And you just let it sit there. And it's like, you could have had two cards instead. You only got one card because you weren't, you know, being careful. So that is sort of almost a common design pattern, irrespective of the mechanic that causes it of does the game have a mechanism to reward the player for being exact about something that's difficult to predict or difficult to calculate. Power grid does it and this game does it. And I wanted to add a new thing to the way we review board games because on one of the panels I was on, this guy was not on a mag vest. Super smart guy, Luke Peter Schmidt at fun to 11 said something really interesting. Most games like most tabletop games, like commercial ones, not necessarily the like weirdo German ones we play, but like in general, they pretty much assume that the games got like three plays, three to five plays tops, and that people really won't play it much beyond that, except maybe bringing it out. Like games that get past that three plays barrier are real close to being classics for all time. If you're talking about games above a certain level of complexity, but below a certain other arbitrary like this, there's a lot more. I'm taking this quote kind of out of context. I think this game has four to five more plays by me before I'm done with it. I think the number of plays this game has is entirely dependent on how many friends you have, because it's like, you might get tired of digging in that bag, right? You know, and it might lose its excitement a bit, but if you bring some new people in, it can refresh in the excitement because reaching in that bag is just so awesome. So thus the way I would put that is this game has three to five plays from a purely intellectual, like I am a gamer playing this game perspective. And any new person is going to want to play it at least two to three times, but it has unlimited play for me as Scott was alluding to in that if I'm with some people who have never played this game and I own this game, I'll just bust it out. Now it's not going to be, you're not going to bust it out with two people who've played it before and one who hasn't. Nope. But one who has played it before and two or three who haven't. Yes, you're going to bust it out because they're going to have so much fun going in the bag. Definitely. And digging in the Egypt and finding nothing dirt, dirt, dirt. And that's everyone's chanting when you go in the bag. All the other players like dirt, dirt, dirt. At one point, Scott took the bag I was about to dig into and put a curse on it.  King Tut got you. King Tut. So we're going to try to do that and all over reviews from nylon of really talk about how many plays this game has got in it. Because I feel like that's one thing that's a metric that we could have used a lot more. We could have used that very effectively in our older game reviews. I'm trying to go back and think about some of those games because then a couple of years down the road, we can go back and say, Hey, how many times did we play that game? Was it really five players? Or was there, was there some like high end strategy we didn't think about? Like, did we play it more? Because like T&E right now is one of the, like T&E and Puerto Rico still have infinity. I'll play those right now. But there's also games that could have a lot of plays and we just don't for whatever reason, like, you know, we could play a lost cities like just whenever. Yeah, we just don't. You know, if we go to anime Boston, we're gonna be playing a lot of lost cities, I think. Good idea. Yeah. And that mushroom game and we're going to bring all the two player games. Netrunner. Yeah. This has been Geek Nights with Rym and Scott special thanks to DJ Pretzel for the opening music, Cat Lee for web design and brand okay for the logos. Be sure to visit our website at frontrowcrew.com for show notes, discussion news and more. Remember, Geek Nights is not one but four different shows. SciTech Mondays, Gaming Tuesdays, Anime Comic Wednesdays and Indiscriminate Thursdays. Geek Nights is distributed under a Creative Commons attribution 3.0 license. Geek Nights is recorded live with no studio and no audience. But unlike those other late shows, it's actually recorded at night.


Hey Mustangs fans, Bobby Trossett alongside head coach Laura Martin after a big convincing win. Coach, when you take a look at the way you guys started off this season, this is probably a pretty ideal way to bounce back. Yeah, I mean our opener was a little frustrating because we think we have a lot of potential and the girls and I both realized we didn't play up to our potential, but coming back today we did some really great things so we're all very happy. It was a 9-0 victory, a final decision over Notre Dame of Maryland. What were some of the things that you were happy and pleased about coming out? We wanted to move the ball quicker and do a complete team effort with some passing patterns and I think I saw that and each individual had some goals that they had set for today and I think we were about 75% successful with those individual goals. This is still room for improvement but we're coming along. Travel to Salisbury on Wednesday, what are those types of things that you can clean up? What's the other 25% in there that you want to clean up? It's all just individual based so we have to really do some more video and they have to have individuals with me or the other coaching staff and then we just slowly but surely step to improve. Obviously Salisbury is a major contender and we're going to go into that free and easy because all the pressure's on them really. You just kick started your ninth season as head coach here at Stevenson so not to age you at all but do you still get those preseason types of jitters and excitement about the season? Whenever the season starts you always get excited to see the freshman and to see the returners see how hard they worked in the off season so I don't think that will ever go away and when it does I should probably step down. That's right, well we're excited to have another season here fully underway here at Stevenson University. She's Laura Martin, I'm Bobby Tross, it's signing off right here from Owings Mills.


hey there friends thanks for checking in today we're looking at the HK45 full-size handgun I mentioned I acquired this in a trade a couple weeks ago very happy with it I traded the Glock 21 and a Remington RM384 the HK45 no regrets there good deal all around but this is a nice handgun no question about it love HK pistols on the pricey side no doubt about that the MSRP on this is 849 it's selling for over a grand in today's crazy stupid market but it does offer a lot of pistol you know for what you what you get it's it's pretty impressive now it does come with two 10 round magazines an additional back strap HK says this is improved over the USP models and one of the things that they make real nice is the ergonomics this has somewhat of a p30 grip feel to it you know the texturing it does not have interchangeable grip panels like the p30 models but it does have an interchangeable back strap a full pick rail on there and two 10 round magazines now they also make this a compact model HK45C compact with a just under a four inch barrel eight round magazines the 10 round mags will fit in that that one this is considered full-size you're looking at a 4.5 inch barrel and you can see it is a full-size pistol no doubt about that it does have the ambidextrous trigger guard magazine release right people either love it or hate it I don't mind it you know I had several guns that use that and it's also fully ambidextrous so not only is that ambidextrous but so is the slide stop or slide release however you want to say it left lefties like that it's a variant one now if this was a p30 or p2000 it would be considered a variant three but with the usp and the HK45 miles it's a variant one double action single action so here we're looking at a single action trigger pull looking at right around four and a quarter pounds with a reset right there no problem with the trigger pull it's actually really nice full-size handgun you get a great feel it is really soft shooting you know they they say they use a reduced recoil system and I'm going to show the internal shortly but before we do that let's check out these sites three dot luminescent sights once charged by light they really glow like brighter than night sights they just come out at you and that's really nice but if it's not charged by light initially then you're not going to get that luminescent effect it also has a thumb safety decocker so here you can safely decock the gun by pushing that lever down if you want to carry it cocked and locked you can do that that will lock up the trigger but it will not lock up the slide four and four point five inch polygonal rifled barrel a lot of room in the trigger guard does have some some serrations up there here to look at the business end of it polymer frame they call it enforced polymer frame that is is really nice HK does a great job with ergos they really do and I've often said that they're just too expensive every HK I've ever acquired was from trade so you know paying those outrageous prices with so many other choices out there is kind of crazy 1 pound 11 and a half ounces we move that to just ounces 27 and a half throwing empty mag on there 31 in a quarter here we have a full mag 10 rounds 45 you're looking at 38 and three quarters how about that carry carry around it's slipping off there well what's going on here okay so just under 40 ounces so you know you don't want a real light gun when you're talking about a full size 45 now I know some people carry this there's some people out there they carry everything you know 1911s Glock 17 like people carry large handguns and many people would carry this a little too hefty for my carry preference it is a nice handgun shoots great love the way it fires I took it out of the box and I was so pleased with the way it performed at the range let's go ahead and disassemble this a little bit different we already checked clear here we'll do that again all you're gonna do is move the slide back to this point on the opposite side that pin right there you're going to push it out and remove it and then remove the slide from the frame here you go lightweight polymer frame double action single action variant one here we have a single recoil spring HK puts that that ring around there I guess that's part of the recoil reduction system and here's your 4.5 inch barrel fits kind of tight polygonal rifle barrel right nice nice look very well machined no problem with that with HK that's for sure I've had several and I have no complaints with the performance of the pistols my complaint is with the price of the pistols so hopefully that will change sometime in the future then we're just going to put this back in place the pin in there first and then move the slide back find that sweet spot there it is and there it is HK 45 full size model 10 round magazines nice ergos nice shooting gun definitely a keeper you know a lot of people own the HK models they keep them for a real long time they like the quality of them I I fall into that camp you know it's just how you are going to acquire them is the issue if you like videos like this please subscribe and share I always appreciate thumbs up button thanks for watching and you guys be safe


The distance was around 40 kilometres from Gurgaon's skyscrapers and the bus terminal at the border of the national capital and Uttar Pradesh. The roads were never as empty. The country and the mega-police had been locked down. It would have taken barely half an hour to cover the distance but for the police checkpoints every five kilometres or so. Small and large groups trudged along the deserted highways resting at places where there was shade. All were poor. Women, men and children on their shoulders, the young and the elderly. The cops told them to go back. Back where? Which home? What job? Where were they going? To homes hundreds of kilometres away. Jhansi, Rewa and Guna in Mandir Pradesh. Many were from Uttar Pradesh. Agra and Aligarh. Barely Banda and Banaras. Eta and Etawa. Gonda and Harpur. Lucknow, Kanpur and Unnau. They were happy that along the way they got water or a bun of sweet bread. Destination? The interstate bus terminus named Anand Vihar. There was no joy however. On one side of the road, the bus atta was eerily empty. The teaming hundreds of thousands on the other side of the road had been made to squat on the ground. The young officer from the Indian Administrative Service held a megaphone and read out names of the destinations. There was a scramble to stand in queue. Each and every vehicle had a deli number plate. Many were tourist buses but there were no tourists. Conspicuous by their absence were buses from Uttar Pradesh. On the way back, the radio crackled. The Sanskrit translation of the Prime Minister's monkey bath was being read out. Narendra Modi apologised profusely to the people of India for putting them to such hardship on account of the corona virus. He had no option. He praised the everyday heroes, nurses and doctors. Not a word was spoken about the hundreds of thousands who had embarked on the longest walk of their lives.