Welcome traders to another TITMIL Weekly Market Outlook for week commencing the 14th of November with me, Patrick Murley. The US Fed officials will have to tread carefully regarding hiking expectations. The low CPI print from the US last week has really boosted expectations that the Federal Reserve will raise interest rates by only 50 basis points in December after four consecutive 75 basis points hikes. However, the Fed will be nervous that Treasury yields fell so far as some market participants interpreted the number as an indication that the Fed's work is nearly done. However, Fed officials won't want to signal that yet as it will reinforce the loosening of financial conditions that could undermine all the hard work in trying to constrain inflation. I expect to hear some fairly hawkish rhetoric over the coming days, messaging that that will likely be for a moderation in the size of rate hikes. Inflation is not defeated and there is likely to be a higher terminal interest rate than the central bank's signal in September. In terms of data, I get retail sales, industrial production, producer price inflation and housing starts in existing home sales. Moderate growth is likely to be the order of the day in the activity reports while the housing numbers will be soft due to the rapid rises in mortgage borrowing costs that have prompted a collapse in demand. PPI should come in on the softer side of expectations thanks to the fully commodity prices and freight costs plus the stronger dollar and easing supply chain conditions. From a technical perspective, Dollar Index traded to our targets. We achieved the 107.72 earlier in the week and we traded just shy of our next downside objective at the 105.97 coming into Friday's closed, closed pretty weak on Friday. So I'm looking for any three wave corrective moves back into the 107.70 to 108.20 area watch. There are traversal patterns there to reengage on the short side. Next down the side objective is 104.42. This stage take a close back through our invalidation level through 108.50 to suggest a more significant corrective move to take place. Moving to the Eurozone, in terms of data this week, we get September industrial production on Monday. 1.5% print last time announced fall from easing supply issues but the outlook is likely to remain gloomy. Heading into Tuesday, we get the November ZEW survey of expectations. Last time, negative 59.7, very challenging outlook ahead in terms of expectations there. September trade balance last time out, negative 47.3 billion energy related import values are still elevated. We also get Q3, GDP, second estimate looking for a 0.2% print there, growth outcomes surprisingly resilient given the headwinds we see within the Eurozone. Then heading into Thursday, we get October CPI year of the year, 10.7 up from that 9.9 print last time out. Our investment is expected to confirm the expanding breadth of inflation within the Eurozone. From a technical perspective, Eurodollar traded to our target on Friday, so we took out that 102.85. So in terms of the next technical level we are looking at, we're now looking for tests of this descending trend line resistance, 106.50s on the upside. So I don't anticipate we get some pullback and some corrective action back into test these prior highs here. So let's say 102.20s, we look for bullish reversal patterns there to engage on the long side. Next upside technical objective for this pattern is going to be the 104.60s. But like I said, ultimately, we're now looking for a move into this daily trend line resistance coming in 106.20s. But step by step. So for now, we are looking for an extension to the upside after a corrective move to target 104.60s. At this stage, it will really take a close back through the 1.0090 level to suggest a more meaningful corrective move is underway. Moving to the UK in terms of the data slate next week, really the focus is going to be on the autumn statement from Chancellor Hunt. Markets have generally given UK Prime Minister Rishi Sunak and his Chancellor Jeremy Hunt the benefit of the doubt when it comes to next week's autumn statement. That's partly because these announcements will be accompanied by new forecasts from the Office for Budget to Responsibility. Something that was lacking when the ill-fated mini-budget was announced in September. Traders no doubt expect the Chancellor to do enough to convince the OBR that debt will fall across the medium term, closing a fiscal deficit that would probably otherwise be 30 to 40 billion per year into 26, 27. Exactly how that will be achieved remains somewhat uncertain and pretty much every possible lever available to the Chancellor has been touted in the press at some point every past few weeks. Recent reports suggest the Treasury will rely more on spending and taxes to do the heavy lifting, but given the real-term cuts in some sizes quite sizable, already facing certain government departments, it may be that this means more noticeable cuts to investment spending for the economy. Much will depend on how much of the burden is placed on consumers by a higher taxation and how immediately those changes come through. We'll also be looking at further detail on how the government intends to restructure its flagship energy price guarantee. The price cap, which had been due to last for two years, will be scaled back from April. Working assumptions are that households will be shifted back to the off-gem regulated price, which market estimate to be around £3,300 annually based on the current futures prices up from that 2,500 at the current government guaranteed level. Lots to get from you and a key piece of data in the UK next week, get jobs on Tuesday, hiring indicators have begun to turn lower, but so far there has been little to no sign of increased redundancies. On Wednesday we're getting inflation reading, famous last words, but October's inflation is likely to mark the peak in the UK's CPI or there or thereabouts. This data will also include the latest rise in electricity gas prices, but given that we're now being fixed by the government obviously until at least April, their contribution has probably peaked. Still, headline inflation is unlikely to slip back into single digits until March or April of next year. And then on Friday, round out the week in the UK with retail sales expect a third consecutive month on month falling sales as the cost of living squeeze continues to bite. From a technical perspective, sterling dollar broke to new highs as we anticipated. Now testing into monthly projected range resistance, 118 to 50s. So I'm looking for any pullbacks now back into the prior highs here at the 116, 16, 116, 50 area. Watch for bullish reversal patterns there to engage on the long side and we're looking for a move up into our target zone, which is this 120. At this stage it would really take clothes now back through this projected ascending trendline support, back through 115 to suggest a more meaningful pullback and we'd be looking at the primary trendline support into the 113. 50s. Moving to Japan from a data perspective, we've got the slate next week. Well, Tuesday we're going to get Q3 GDP. Last time out 0.9 percent looking for a weakening there to 0.3 percent. Weak trade is likely to offset support from consumption. We'll also get September industrial production. Last time negative 1.6 percent and that's the final estimate greed coming into that print. And then on Wednesday we'll get September machinery orders. Last time negative 5.8 percent looking for a positive 0.6 percent. Volatility continues but points to downside capex spending risk. And then we ran out week on Friday with October CPI year over year. Last time out 3 percent looking for an increase there up to 3.7 percent. Price pressures are building and the BOJ is certainly focused on wage inflation. From a technical perspective, Dolly N traded through our target so we hit the 143. 20s traded through the 141. 13s and as of Friday, I was looking for a test of this 139. 20. We got that and exceeded it to the downside. So looking at new technical setup or new pattern that I'm tracking now, I'm looking for a corrective move, three-way corrective move. Something similar to what we saw into the early part of November before we get that next leg to the downside. So we are going to look for some support to develop around this low here, 138. 30s. Three-way corrective moves back into the midpoint of the channel here. So something around 141. 50s. From there, we watch for bearish reversal patterns to re-engage. On the short side, targeting a move down to test the equality objective into that 136. 14s. And we can see here we have high volume load on the daily time frame just below there. 135s. So like I said, bearish reversal patterns into that 141. 40s, 141. 50s to target those downside objectives. From an validation perspective, really, we need to close now back through this trend channel resistance, which is likely to come in at the start of the week, back through 145. So I might need to see that early in the week and we're looking for further downside extension. Heading down under to Australia. In terms of data, get the RBA minutes on Tuesday. More color on that November decision. Also get October overseas arrivals. Net temporary visa arrivals are starting to pick up in Australia. Then on Wednesday, we get October Westpac leading index, last time negative 1.15% pointing to a material loss of momentum really heading into 2023. We will then also get Q3 wage price index, last time 0.7%. Looking for a positive 0.9%. The outsize increase in minimum wage should be seen through the last quarter's data. Then heading this Thursday to October employment. Looking for a 15k per their employment growth. Average just 200 per month for the last three months. And firm participation points to arise in unemployment. So we should see an employment rate coming in 3.6% versus 3.5% last time out. That rounds out the data down under in Australia. From a technical perspective again we've achieved all our targets in this last week. We're looking for that 6590 got that and then we have a upside extension to that 6660. Now we're looking for is a test of this daily trend line resistance. Currently coming in with the monthly R3 at 6770. So any pullbacks early in the week to find support into the 66th handle and we're going to target a test up into that 6770 trend line resistance. At this stage the validation point for this thesis would really only come on a break of this trend line support. So way back down at 6450s. So let's round out the outlook with a quick look at our weekend risk barometer. Bitcoin obviously torrid week last week with the FTX meltdown. Finding some support here now at the 16300 level. As we retain support here we look for an upside extension. An equality objective corrective equality objective target is 18872. At this stage any close back through the 16300 bearish development and we are likely to re-test loads into 15500. And that concludes the weekly market outlook for week commencing the 14th of November. As always trade us, plan the trade, trade the plan most importantly. Manage your risk. Until next week, thanks very much.


All right, I guess we'll go ahead and get started. Welcome to this morning's panel. This will be a true panel. We're not going to give any talk. So I want it to be conversational. There will be time for audience questions after we run through our questions. And so please, if you do have questions for any of the individual panelists, just hold them till the end, and then I can run around with a mic. We are being recorded, so I don't want to have to interact with, break things up to get a mic out to you during the presentations. So my name is Harrison Decker. I'm an associate professor and data librarian at the University of Rhode Island, and I'm the director of the library AI lab. I'm going to introduce our panelists and then just get started right away with asking them questions. So the gist of this morning's panel is that I wanted to address. And this is sort of prompted by a comment someone made to me who works at a library that is fairly heavily invested and offers great and extensive programming training in languages like R and Python and a variety of other, and GIS and a variety of other techniques. And they said, despite the reputation, despite everything they're doing and all their success stories, they still get questions from the university provost about why is the library actually doing this. So that got me thinking, well, maybe this C&I would be a good venue to have a conversation by four practitioners who are very heavily involved in just doing this sort of thing. So to my right, I'll start with our first panelist, and he's Matt Burton. He's Matt as a lecturer in the Department of Information Culture and Data Stewardship at the University of Pittsburgh School of Computing and Information. His research focuses on digital humanities, scholarly communication, and computing education. He teaches design, data science, and web technologies in the master of library and information science program. And he has a PhD from the School of Information at University of Minnesota. Sorry. Let's make that University of Michigan. Yeah, yeah, yeah, yeah. He does have a PhD, though. OK. Seated to his right is Vicki Steves. Vicki is a librarian for research data management and reproducibility, a dual appointment between the NYU Division of Libraries and the Center for Data Science, and her research centers on integrating reproducible practices into the research workflow, advocating openness in all facets of research and building, contributing to open infrastructure. And she is the co-founder of the LIS Scholarship Archive and Open Source Open Access Repository for LIS and Alive Fields. She also works on the ReproZip project, an open source tool that enables full computational reproducibility of research and TAGET, a free and open source qualitative analysis tool. Then we have my colleague from URI, Indrani Mandel. She is a lecturer for the Department of Computer Science and Statistics of the University of Rhode Island. And she is the education coordinator for the URI Library AI Lab. The Library AI Lab is only a year old, but has already been in the headlines for its undergraduate research and outreach programs. And Indrani's interests are machine learning, data science, and AI education for K-12 students. And then our final panelist is Tim Dennis. Tim is the director of the Data Science Center, a research and education support unit of the UCLA Library. He is an instructor and instructor trainer with the carpentries, an instructor community that teaches foundational coding and data science skills to researchers worldwide. He recently worked to establish library carpentry as a lesson organization within carpentries and serves on its curriculum advisory committee. Tim's interests include research transparency and reproducibility, open data, and inclusive pedagogy. So to get started, I'm just going to go across the panel and we'll just, I guess, start. We'll start at the far end and work our way this way. I just want you to briefly talk about your own educational background and the work you've been involved with and what sort of specific programming topics you've taught or computation-related topics you've taught. So start with Tim and work our way down. So my educational, I guess, background, I have a master's of information management and systems from UC Berkeley, which is called iSchool now, I think. And it's part of the Data Science Division, which is relatively new there as well. And when I consider myself really a data librarian and I'm self-taught as far as programming, I learned a lot at work with Harrison, or moderator at UC Berkeley, and learned a lot from him since he has a programming background. And at Berkeley, I learned a lot because there was this data science kind of renaissance that happened there as part of this Sloanmore funding for data science environments. And it was really highly generative. And I've been taking what I've learned there and applying it to other places like UCSD and now UCLA. The programming topics I've taught are Python, Git, Bash. I've taught data visualization and those tools. We've recently, I've taught text data mining and R. So we try to cover a wide gamut of things and I'm actively involved in developing the Carpentries Program at UCLA. We're a member of the Carpentries. It's like a member organization like CNI itself. And we're developing, you know, more trying to scale up our number of instructors on campus and teach more of the workshops. So I think that's kind of. So my journey started in India. I am an engineer by training. And I'm an electronics engineer. When I moved to the United States, I joined University of Rhode Island and that's where I went to grad school. That's when I switched to computer science and got interested in machine learning and data science. Currently, I'm working there as a lecturer and I teach computer science, core computer science courses. Additionally, I also teach programming languages at the library. I teach R, Python, machine learning. And I also work with undergraduate students interested in research at the AI lab, which is located in the library, which is a nice thing because we get students from very diverse backgrounds. As for me, I also came into my higher education thinking that English or the classics would be the best thing to do before library school. And shout out to Nanette Bayer, my old advisor, was like, oh, you should actually do computer science and just try it, see if you like it. And so I was actually sort of coerced into starting this journey that way, although very happily. So that was where a lot of my background came in with learning some of the basics. And then when I started, I actually thought I was going to be a corporate librarian. And that was totally disrupted again by NDSR, the National Digital Structure Presidency, where I got a really deep dive into working with different types of data and computational processes. And that got me really interested in the way that we are teaching new researchers how to conduct their work. So as a part of my role, I teach all of the programming languages and concepts that my colleagues here teach. I would say one of the things that I am also teaching are things like how to ask questions of data for machine learning or AI purposes. I have a lot of consultations with folks who are like, how do I make sure that when I'm fitting some data for a model that I'm not completely biased? It's a hard thing to answer. So alongside of the computational literacy stuff that I think is really important, we're also seeing a niche data literacy on the rise as a part of supporting data science as well. All right, so I have a very long circuitous journey, so I'll try to keep it concise. I have an undergraduate Bachelor of Arts in bioinformatics. And so I have some formal computing training, but also situated in a science context of scientific computing. And then I worked in industry for a while doing information security, and actually wasn't really on a path towards libraries at all. And then one of my office mates was like, this information, we were talking about the challenge of information security. And he's like, this is really actually a library science problem. And I kind of pivoted and ended up going to the University of Michigan at the School of Information, which kind of introduced me to the information schools world, where I am today. I ended up getting a PhD in information and doing a lot of work around. A lot of my dissertation work was in kind of science and technology studies, and actually not computational at all. I didn't even tell people I knew how to code initially. Because one of the things that happens is if you know how to code as a grad student, you very quickly get grabbed by a thesis advisor because they desperately need people who can code because they don't know how or they want people to do that work. And I saw that happen over and over again. And I've seen that happen. One of the things I started doing at Michigan is trying to address this issue. So I started working with some faculty on developing kind of a computing education workshop, kind of similar to the Carpentries, although I developed a little bit independently. And then I did kind of get involved with some of the Carpentries movement. And then since moving, then I moved to the University of Pittsburgh. Initially, I was actually jointly appointed between the library and the school. At the time, it was the School of Information Science. I helped launch a digital scholarship unit. And we actually created a workshop series that was a joint workshop series between the School of Information Science or what was the School of Information Science and the libraries on kind of running workshops on technical topics like web scraping and data visualization formed the School of Computing and Information. I moved full time, so I'm teaching faculty there. And I teach a bunch of different computing. Computing is, I teach a bunch of different programs. So I teach in the undergraduate computer science program. I teach in the Masters of Information Science. So I teach kind of big ideas in computing and information, which is kind of a conceptual course for the undergrads, but also has a skills component to it, which is like teaching undergraduates, how to do Python and Command Line and Git. And this is computer science undergraduates who actually never learn that. They're just kind of expected to know it by the faculty. So they're like, well, why should we teach them the Command Line? They should already know how to do that, which is a very non-inclusive approach to some of these kind of very basic skills, which I think is that attitude and that need for the kind of addressing the basic skills in a semi-formal setting and for all of the students as a requirements or as a necessary step. So I've also developed and worked on this research computing education initiative that is running an eight-part workshop series called Data Basics, and that's actually for graduate students in the disciplines who don't have an opportunity to learn, because the basics, so we teach Python and Pandas, which is a data analysis tool. We teach Jupyter notebooks and the Command Line and Git. So kind of a lot of the similar topics to the software carpentry in this kind of eight-part series that's open and it's free and open to anyone in the university who wants to take it, the graduate students, faculty, and staff. And the goal here is to kind of give, it's mainly graduate students, doctoral students, master students in the disciplines and sciences and social sciences and some of the humanities too, to kind of learn those basic things that they wouldn't necessarily have an opportunity to learn because it's not taught in formal coursework. So I teach that and then the other final piece on computing education that we are working on as we've, at Pitt, we've recently redesigned our Masters of Library and Information Science and one of the core components of this redesign that we're still working on is putting and embedding computing education for the library science students. And we're still, this is going to be a multi-year journey but one of the things that I want to see, the library science program is kind of an alternative path to learning computing for people for whom the path, the traditional path in computer science or in information science, that door gets closed or they feel that they're not included or are able to participate in those communities which is a recurring issue. So we're trying to kind of, and one of the key components for the library science program, I haven't really figured out, we haven't figured out how to do this but there's a huge demand for computing education and computing needs broadly in higher education, in K through 12 and generally. And so part of what we're trying to do is not only teach the library science students computing topics but also equip them and teach them how to teach. So kind of a train the trainers thing because Pew had a study that people are looking to their librarians to help them with computing education topics, especially in public libraries. So if we can kind of equip the students with these skills then they can kind of go forth and help address these broader needs. So to kind of jump right into the, to ensure that we actually address the topic of the panel, I would like to just toss out the question to the panel. From your experience, what are the pros and or cons of teaching these types of topics in a library? Just quick thoughts on that. And I don't know, I'll just, someone want to start picking. Oh, you're looking right at me, okay. We'll start with Vicky, but they'll never just jump in if you have something to say. Yeah, so teaching data science topics in the libraries, the pros and the cons. So the pros I think that Adrani was mentioning is that people feel like the library is a space where they can come and fail if they need to. So a lot of folks who are coming saying, I don't really know the basics of AI or machine learning topics or predictive learning, whatever, natural language processing, text mining. I don't want to go to this workshop in the Center for Data Science because they're going to assume that I know every, like all the toolkits walking in and I really don't. So the library becomes a space where they can try and fail at these new topics, which is a key part of the learning process. So I think there's sort of a safety net in knowing that I'm here to provide you the service and I'm not judging your computational ability. And in fact, I'm here to scaffold you so that you can reach that. One of the cons is like the opposite where people are like, why does the library know or care about this? Which is what prompted the panel. So like how would walking into the library and seeing me, like they don't equate that with data science expertise for lots of reasons.  So that sort of cultural problem is yet to be addressed. I think across the board for computing education, but especially in data science where the demographics are so homogenous and badly skewed. I would like to add a few more things. When we run our data science or machine learning or natural language processing workshops, we have professors, graduate students and undergraduate students all taking the workshop at the same time. All of them have similar questions. Again, what Vicky has mentioned is a safe place. Everyone is treated equally and they feel that I'm not an expert. I'm here to learn something and it's okay to ask any kind of questions. We, including myself, when I go to a workshop, I don't feel like I'm a faculty. I feel like I'm going there as a student to learn something. And it's a great opportunity because the libraries are really providing this neutral ground where we can all go as students, as someone who is interested in learning a topic. I can add too, I concur that, I remember I saw a talk by a Fernando Perez, who's kind of the guy who started Jupyter Notebooks and he had a big slide, why they chose the library at Berkeley to put the Data Science Center Data Science Initiative in. He says the library is like Switzerland. So it has like a neutrality, like Gianni was saying, that is more welcoming. It also, I think for me, there's a virtuous cycle of, so data services have been part of libraries for a long time. Now it's, the nihilism is data science, right? But it's been doing a lot of this service and consulting for a long time in libraries. And there's a virtuous cycle that happens when you consult with people over time, you see all these use cases across the disciplines. Oh, people are doing text data mining across many different departments. So those use cases kind of can formulate and become a curriculum, right? So you know what to teach. People are struggling getting beyond their laptop if they open a data set with eight million rows and it doesn't open. So what are the kind of heuristics with that? When do they need to move to kind of cluster computing? When do they need to do some refactoring of their code? So I think that's a rich ground bed for addressing common problems in academia and then you can develop lessons and teach and include a diverse set of people that are across disciplinary. So I think that's a real strength of libraries. I think there's a credibility issue that might be the con. So like who, how do, you know, I don't have a CS degree. So why should anybody listen to me when I'm telling them how to code or whatever? But I think it through kind of a reflective practice when you work with people, they, you develop a good word of mouth and they'll come back to you. So I think libraries are a great place for teaching this stuff. One of the things that, you know, so I'm mostly teaching, you know, I teach and I'm a faculty member so my like incentive structure and my organizational life is kind of governed around terms and teaching classes and the needs, however, don't necessarily conform to that structure. And so one of the things I'm trying to do is collaborate and work with the library and also work with our high performance computing center and try to like kind of create this triangulation of people with, you know, the HPC as a computing infrastructure, the School of Computing and Information, we have some of the expertise and the library has kind of physical infrastructure and also temporal, a different temporal rhythm that I think, because one of the things is after the classes and there's always that, okay, I need, you know, so I loved your workshop, how do I do Python package management? And it's like, you know, and how do I use this or how do I install this specific package for this because I'm a biologist or I'm a molecular and biophysics graduate student which I just had a whole bunch of them in my workshop. And I don't have the ability to kind of do that support work, just there's not enough hours in the day. And so, but that's where I think the library can help because I think the key component of this is to build a learning community within the university. And I think that's the university, you know, the library is, that's what they're structurally set up to do. And so they can kind of, the hard part though, the con, you know, that's the benefit and the power and the potential, the hard part though is just trying to figure out the organizational configuration, like, you know, who's gonna pay for whose time, how are they, and library to their credit spends a ton of time thinking about the organizational sustainability of a service to the, she ran some of the grad students and faculty who were trying to work on this and they just wanna do it on the librarians. They're like, well, slow down, let's think about it. So there's a balance there, but I think when it, you know, when it does come, when everything can come together, I think it can be extremely powerful. So to pick up on that idea of learning communities, one of the, I think one of the limitations of this mode of instruction that we were kind of talking about for the most part, being the workshop format, is inherently problematic with these technical topics that, so because you don't become a Python programmer in a two-hour workshop. So I would like to ask, like a very practical follow-up question, and that would be, what have you done to address some of the limitations of the workshop format? For example, dealing with prerequisites, scaffolding, finding times. Just scheduling is very hard in the university setting because everybody has their regular course schedule. Students might wanna take something at night, but grad students and faculty might have outside lives that require them to go home. So can you talk a little bit? I wanna go home. Did I say that wrong? Anyway, so do you wanna go home now? So anyway, what have you done to address some of these limitations? I have taught the workshops in the evening. Actually the grad students really want it in the evenings or some of them do and then some of them can't. So I teach, sometimes I do the workshop series during the day and sometimes in the evenings. It kind of depends on if I have other evening classes. In terms of scaffolding, the workshop is very much a basics. And up front I tell people, I mean we've extended it so it's like eight, three hour sessions so that's a little bit of a longer engagement. So I kind of do set expectations up front. One is I don't have any expectation that you have experience with this. I have everything set up. They just need to show up with a laptop that has a web browser and then everything is running in a Jupyter Hub in the cloud. All the materials are on Git and automatically get loaded. So that's kind of a technical answer to some of this, which is they don't have, there's not this like getting everything installed and set up stuff so I kind of scaffold them in that way. But I do set expectations on you're not gonna leave this workshop like an expert Python programmer. One of the benefits I have is the students are super motivated because they're opting in to take this. This isn't a required class per se. It's an open free workshop so they are personally motivated which is not always the case in a teaching context but I do have that benefit so they work on that. But again I think the next step is how to, how to give them the ability to continue and I haven't really figured that out. I've had conversations because one of the things is like faculty will be like, we want all of our doctoral students to take your workshop and it's like, okay, that's great, they do it and then what changes are you making in your curriculum to then reinforce what they learn? And they're like, well, we can't do that. Our curriculum is that, we can't change it. So it's like well then it's not gonna work. I can't do this, there has to be substantive change in the disciplinary curriculum and classes have to kind of, okay, they'll have basic Python but you need to teach them the methodologies because I'm not teaching them the methods and you need to infuse them with the computing that they, you can kind of assume that they have and reinforce it so. Yeah, well I wrote down Jupiter Hub and Expectation Management so pull that thread a little. The thing I would add to that is in dealing with the workshop format, something that's really important to me is to build out open educational resources that they can always come back to after the fact. So all of my sort of data science focused classes are applications based. So I'm not, I don't teach like this is what a variable is in Python. I say like this is what a variable is and you're gonna put your part of your data set into it and so everything that I teach is, has the pedagogical underpinning of like you're gonna, you could modify this code lightly with a different data set and be able to work with it and that relies really heavily on me creating a larger scale open educational resource to point them to after the fact to walk them through some of the other projects. I would also say distributing the actual teaching like I collaborate with some assistant faculty at the Center for Data Science who come in and teach some of these intro workshops as well and we co-teach a lot of them and I think that also signals to the students something that there's a lot of support for in the university so they could come to me, they could come to this faculty member and I think that's really powerful and empowering to them when they're in the middle of like an intro ML workshop and they don't understand probability and et cetera. I have noticed running the same workshop twice is often at different times of the week is beneficial to a lot of students because scheduling is a problem. When I have the time, the graduate students might not have the time or the undergraduates might not have the time at the, so running it twice once to maybe during the work hours one maybe after five o'clock is a good idea or running it on Fridays between two and five has worked well for with our undergrads for me as far as prerequisites are concerned I have it uploaded on GitHub so they have access to previous lessons. I always like to teach them in a series of workshop that means you don't walk out of the workshop after two hours feeling like you are an expert so you have to keep coming back. That is an expectation I said in the beginning of the workshops and I have had great success with students returning for the entire series. Yeah, so there's a number of different things you can do to help with pre-wex or tech setup. You can have install fests, I've done that before. Like in libraries I like to get our systems people involved if they can become helpers at your workshop get people set up and configured. I taught an African American Studies class that's on the mass incarceration phenomena this last quarter and because it's for undergrads I didn't want to have all the setup stuff and I taught it completely in our studio cloud which is a cloud based R tool and that worked pretty well because I could distribute the notebooks to them and they could work through it with me. I always teach live coding style and I have a bunch of exercises within the class so people have points where they have to work through a challenge or stretch themselves or apply what they've learned. I think that's very effective. The last thing I've done is partner with specific departments. So we have a good relationship with the masters of social science and the masters are urban planning and we teach these workshops because you want to encourage peer learning and peer teaching. So if you get people to come from the same department together and you reserve some seats for them then you can kind of get that happening because they know each other and they feel more welcome, they build confidence and then lastly on the feedback loop you want to have them come seek you out for consulting or to get help because they're invariably going to want to do their research and then they've learned some basics in R or Python but then what is the next step? Oh I want to apply this. I'm having problem with this package in R and then they'll come get help from me. So you want to see it as a whole cycle. Teach a workshop, you get consulting, teach another workshop, you do it, try to encourage peerage and that works kind of well. Can I add one thing super briefly is that I always show how to debug in my classes. Like I will often intentionally put errors into my code to be like, oh no I have no idea what happened, let's go to the internet and find a solution and I think that approach also really helps students after the workshop to be like oh, I saw someone like who is my instructor run into a problem and solve it and follow the same steps and they can probably figure a lot out after the fact too. Yeah that's an excellent. I mean basically we are, like in the carpentries do now encourage people if you're really good typer which I'm not or a speller which I'm not. So I have error all the time but if you are that we encourage people to introduce error because getting over error, the fear of having errors is the real big thing and having kind of persistence and resiliency with that is something you really want to come out of a workshop. So definitely. So let's shift gears a little bit because I suspect some people in the audience may say these sound like great things but we don't have the staff to do that. So let's talk about the staffing side for a little. So one is I mean we could probably spend a lot of time talking about what's missing from the MLS curriculum. So I don't really want to go down that path although if anyone wants to bring up any ideas or if you know of new curriculum that's being introduced but since we have a couple of people here that are doing things kind of on the margins of libraries or between in the intersection of libraries and computer science, maybe and I'll address this to Andrani first, talk specifically about what libraries and librarians could do to attract more computer and data scientists like you to come and work with us. I think the initiative needs to come from above like the administrators provosting to encourage this environment of learning and data science and computer science and encourage libraries to set up this kind of units or spaces, providing I was at another talk yesterday and they just created this space which attracted lots of students which would not have happened if that space did not exist. So URI has been proactive with it and our provost and libraries dean who is here has been very supportive. I am among the three faculty who are associated with computer science and libraries. What would attract us is providing us this environment and this opportunity to work with diverse set of students. It really helps us. We don't usually have access to this diverse set of students when we are tucked away in the computer science department or in the engineering department. Library really helps us to find the students and this diverse projects. We have had great success over students from nutrition, biology, social sciences to name a few. Yeah, so it just provides us with this incredible amount of wealth where the students are our resources and where we can be helpful and useful to the community. Matt, would you like to add anything to that? So the question was making computer scientists. This is something that we are very, yeah, so actually when I first started at the University of Pittsburgh, I was in a jointly appointed position across the library and in the School of Information Science as kind of an explicit effort that's similar to kind of connect the department, the academic department with the unit, the library unit. And there's a couple of us and that's kind of we worked on developing the Digital Scholarship Services unit at the library. We don't currently have anyone in that position but I think actually I'm really interested in these kind of joint appointments and kind of creating that space. I mean, this is something that we are trying to do. I mean, that relationship is also working itself out in the School of Computing and Information because we're computer science, information science and library science all together in one academic unit and we're all very different and so we're all kind of sorting out and building up those relationships. And this is a new school that's just been recently formed in the last couple of years. We are also hiring teaching stream faculty, tenure stream faculty. We are looking for graduate students. So if you know, if you're, yeah. Anyway, come see me, we are, because like the, we're doing this, I'm all this stuff I'm kind of doing on my own. On a very lean, I get one course released kind of work on all these workshops and stuff. I get somebody, and a lot of it is just personal connections. I have somebody from the high performance computing unit who is a friend of mine who like helped me spin up the Jupiter instance. And then I have connections and people in the library who are, and it's like a lot of it is like personal favors and just personal relationships. And I think now it's, now we're at this place of like how do we institutionalize this? And that's kind of where we're at right now and taking that, I'm really good at like finding the people who are on the ground who are gonna do the work, but now it's got to socialize up to the administrators to kind of get them to do buy-in. I forgot to mention another thing. We also have Joan Peckham here with us who is a, would you please raise your hand? She is a professor in computer science department and also works with us at the library. Yeah, it's another approach you can, it at least has been effective for me is that, and you know UCLA is a big library. So we have like a lot of programmers, one of which has a master's in machine learning and computer science who works in our software development team and so he's become a carpentries instructor. So he teaches with us and he also consults. And I have in my unit have two programmers who are now kind of being refashioned into data science specialist. And part of that is we provide training and support for that. So look within, if you have a big organization, look within to kind of fill some of these roles. And if you have a digital library like we do that are doing kind of machine learning on their collections, that's a perfect way you can kind of externalize that as a service and get those folks to help consult for your unit or build a unit, right? So this can be research facing and not just internally facing. So that's what we're trying to do. Okay, I wanna, I know we've been talking about the carpentries a lot and I wanna make sure we talk a little bit more about that in case people aren't familiar with it. I can't remember when I first learned about the carpentries but I think it's safe to say as probably one of the early librarians to go through instructor training six or seven years ago. And at the time I, this was at UC Davis. And at the time there was one other librarian involved and she was unique in that. She was a former research scientist with a PhD, Amy Hodge from Stanford. And I thought, you know, this is great. And this is like, this would be ideal for librarians but the cynical part of me thought ain't gonna happen. You know, librarians are busy, they're skeptical about some of this stuff but then they proved me wrong and now it's really taken off. So many, I think I was just at a meeting where they were telling me they had like 20 certified carpentries instructors or something at their library so it's really kind of exploded. So Tim has been really involved with the carpentries. So maybe just a few words about the carpentries movement and their pedagogy and a few thoughts on the importance that plays especially for those who aren't familiar with it. And maybe what sorts of things librarians have been doing in that program? Oh, yes. So as you mentioned, I think librarians have been involved in the carpentries for a good while and various roles but so the carpentries is a global, it's a volunteer network of instructors and we teach like basic and computing and data skills to typically researchers but now information professionals, librarians, research staff on how to do their research better, how to automate tasks, how to be more reproducible in their work. So all these are kind of wrapped up into the carpentries and there's three carpentries, the software carpentry which teaches best practice in software development techniques because a lot of researchers get into doing their research and they have no background in CS, they don't know how to write scripts and so it's teaching some of those best practices. Data carpentry has geared it. More domain based so there's data carpentry with genomics and social science and geospatial and it's geared to the methodologies for those specific domains and dealing with too much data that researchers don't know how to handle it. How to manage it, how to analyze, how to do data visualization and the library carpentry which I've been involved with on the governance committee and now we're the latest carpentries so there's three carpentries and then that's more geared at teaching librarians how to code and deal with data, how to kind of automate their own work processes and then be able to better engage and communicate with technical staff in their libraries and also be able to engage better with cutting edge research, right? So those are kind of the three carpentries and if you wanna become an instructor you go through a training program that is really not about technology it's about how people learn and how to teach and we use kind of education psychology best practices on those kind of facets and you go through a two day workshop and then you become certified as an instructor. I think that's, and then libraries, yeah so the library and all the library carpentry has been like three or four years old and we're, I think there's a lot of also members of, because carpentries is a membership organization a lot of libraries that are members of the carpentries so it's kind of taken off and like within the UC system I think we have like probably 20 librarians who are certified and we chat, teach workshops together so it's kind of an active environment. Yeah, one of the cool things about the library's collaboration with the CDS, the Center for Data Science at NYU is that we actually split our carpentries membership so it's supported by both organizations which has been great for me because I don't have to do all the administrative work I can share that too but also just this deep understanding that like it's a great place for learning to happen in both of these multidisciplinary spaces which has been very cool to see. I'm sorry I should, I forgot something but one of the big components of the carpentries is we really want to have and it's called inclusive pedagogy where we really have a warm welcoming environment where people can come in regardless of their background and build confidence and have a positive mindset so they can learn and grow, right? So we have a really serious, we're serious about our code of conduct and we do have a real like, it's been pretty successful in this mission. So now we have, we're translating lessons into multiple languages, we have an African task force has been really incredibly active and wonderful like the work that they've done in the continent of Africa. We've had workshops on all seven continents so we really have a global reach and it's really premised on being inclusive and to try to improve the representativeness of people in computing and data science as well. So yeah, so anyway. So before I open it up for audience questions I want to just go through the panel once and give you an opportunity to mention maybe just pick one sort of new and upcoming program service training offering that's code related that you are, that you have in the works for the near future. So that's relevant for you. Just to give you a chance to promote something, you know what it is you're up to. Matt, you want to start?  Your job's a little different but...   I've had a couple people come and say that and I was like, oh, wow, that's a nice feeling. And, you know, so trying, you know, I've been working on kind of getting a large-scale deployment so that we can do teaching, you know, because as a platform for teaching, it's just, it's really fantastic. The computer scientists don't understand it or like it but everybody else, the scientists and the social scientists and everyone are like, well, it's just a no-brainer. So, you know, that's, and they're the ones who are like really using it so, you know, trying, you know, building and so I would say one of the things that I've been, the thing that I've been looking at and kind of using as inspiration is all of the work coming out of UC Berkeley, both on kind of the infrastructural Jupyter hub deployment stuff that they've done but also they are developing curriculum materials, you know, in and also talking about how to teach with Jupyter notebooks and so like all of that stuff. Everything that's coming out of Berkeley, out of the Berkeley Institute for Data Science on the stuff that, it's not something I'm doing but it's something I draw on so, yeah. That's the data aid course. Yeah. Yeah. I'm also, I want to address like one major challenge of supporting data science if I may, which is something I want to have a conversation about. The hardest part for me in supporting data science is collecting, like actually providing data. A lot of the data sets that students email me how like a Google site with a million images of a face and the person who runs it needs a signature from a department head but we might want to collect it and give people access to it in the library and it's one researcher maintaining that and they're not equipped to deal with like library's collection stuff or even like be willing to have a librarian broker that agreement. So for me, the thing that I really want to work on moving forward is almost less on the pedagogy side and more on sort of the maintenance and infrastructure for collection to support data science. So that's like my major challenge that I'm hoping to address. So we at the URI AI lab are interested in K through 12 programs, especially in machine learning and artificial intelligence. We have just hosted our first AI summer camp for kids ranging in age from seven to 17. It was a huge success and one of the summer camps was absolutely free and we identified there was a need for AI education among this group of students who belong to the underserved communities. We definitely identified a need there and we are working on a couple of projects. One of them is an after school program where the students would come to the URI library and have this two hour session instructed by one of us on artificial intelligence and machine learning. Apart from that, we also reach out to schools. We do talks and just raise awareness about artificial intelligence and try to break some of the myths about artificial intelligence and machine learning. Yeah, I was trying to think.  And the other thing I'm talking with some colleagues is about like data ethics and ethical concerns around machine learning and these kind of black box systems of algorithms and we're talking about having a workshop series around that. Okay, so are there questions from the audience? I need to bring the mic out because we're being recorded, so great. Thank you again, that was an awesome presentation. Quick question on how you entice students. I know you said they sort of come to you but do you and you're talking about outreaches, are there ways that you reach the students in non, say the non-computer science, non-engineering students, some hackathons, those kinds of things and how do you physically do it?   I actually mainly see non-engineering and non-computer science students in my like intro machine learning and intro to getting things classes. A lot of times it's the students outside of those disciplines who feel like they're behind and wanna catch up even though it's not the case in reality. I'm sure the computer science students would benefit quite a bit from coming. So I actually think that when I'm looking at sort of my metrics of who's attending, it's mostly master students by far and mostly in non-engineering and computer science disciplines or people doing computational research but not in COMSI or like computer engineering. I have a few things to add to that. We do actively contact professors across campus and try to advocate data science and machine learning to be integrated into their course curriculum or at least talk to their students about this workshop that are available so that they can attend this workshops. And we advertise them heavily through our university newsletter on every digital media that's present across the campus. As far as our outreach programs are concerned, we work with Office of Diversity and by now we have several contacts with schools directly and they have approached us to coordinate with them and then we also attend events all across Rhode Island. I am just the dean of graduate students. I'll just, the deans, whenever I run off of the workshop, there's just a forum to sign up so I'll just email the various deans and say, hey, distribute this to the grad students and then it just fills up. And actually last term I sent it to a waiting list and it filled up after a day and I actually have no idea how they found it. I think the demand for the computing, like the Python computing stuff is so great that I just put it out there and it fills up which I actually don't know some of the under, I think it's just like every term it'll be a different, like it'll be engineering heavy or business heavy and I think it just, it gets circulated amongst the graduate students. But the way that I've started is by emailing like the dean of grad students or like the dean of research at a different school and they'll distribute it. Usually I ask them, can you distribute this to the grad students and that's how I've been reaching the graduate students at least who have been the primary students, people taking it. So let's take one more question because I think we're just about out. Thank you for an excellent, excellent panel. Jane Greenberg, I'm an high school professor too and we have, we've incorporated data science, we have data science certificate now and I lead a program called Leeds Library Education and Data Science. We've now graduated 21 fellows in the area so we're really into this space but it's a little self promotion but sorry, we'll spread the joy, right? But my question to you and anybody in this room is dealing, not everyone here is a librarian but we are seeing these services needed in libraries and how do you get this into things like the ALA accreditation? These conversations need to happen. We just got through our accreditation and it's a process and we all, for people who have an accredited ALA degree it's still very important. I know some schools don't do it anymore. There's a long debate there. But how do we, this is really important for future librarians and so I would be interested in any comments people have on that. Thank you so much. So we're doing that, we're going through accreditation right now and so we'll see how ALA responds to some of the changes that we've made to our curriculum. But I think this, for ALA the standards, there's the set of standards and you can describe how you meet those standards and what evidence you marshal to do that. So I think part of it is there's interpretation that happens in what those standards mean and I think we'll see how they respond to the changes that we've made. But I think we just have to, I mean I personally think there is some flexibility in that accreditation process. I don't think it's in ALA's best interest to not accredit previously accredited institution. I mean, so I think maybe there needs to be a little bit more, this is being recorded, but a little bit more risk taking on the part of the faculty who are doing that accreditation process. But it is, I mean the problem with that is the process is so incredibly, it's not, it's very involved to, there's some aspects of it to the credit, but there's some of it that it seems like it's just a lot of paperwork and so like maybe, I mean this has been a reoccurring conversation in the high schools about how to streamline that process and make it more flexible. But there's a lot of programs that are accredited by ALA that have this data science. I mean Michigan is a good example there. The whole program is accredited by ALA and you can go through that program and take only data science classes. So I think there is the space there, but it is a hard process. That's a little scary that there's only, they can go through to get a library degree with only data science classes. That is, it's ALA accredited. It's ALA accredited. They don't typically go to libraries. They go to Google, but the whole program, that's a structural configuration. God, next panel is gonna be about that. So I wanna pull that thread for like five, four hours. Let's take one more question. At Pitt, you have to take, at Pitt though, you have to take the core library. It is 10, so people wanna run to break, but we have. Reference. Good morning, my name is Teresa Byrd. I'm Dean of the University Library at the University of San Diego. I am on the board, the accreditation board for ALA for library schools. So I certainly enjoy hearing your comments this morning. Not sure you can, we only accredited the MLS programs. There is no MLS program in Michigan, it's MSI. Then there has to be a track or something because I'm agreeing with her. We wouldn't credit a program that is not solely not. But you don't credit tracks, you credit programs. But your comment from the library school faculty member, I think whatever you would like, I can, you can share your information with me. You can give it to Karen O'Brien. We are looking at the process, I have to tell you. I read all of those documents and it's a lot of stuff to read. So I am certainly in favor of streamlining the process and we are looking at that right now. So thank you, I just wanted to share that you had an accreditation person listening to your comments. Thank you. All right everyone, thanks for coming. Thank you. We're all around and feel free to chat with us. If you'd like.


Thank you to the people of D-BUD for sending me these amazing volume adjustable earplugs. So if you're like me and you really struggle with the sensory distress caused by all sorts of places whether it's the gym, whether it's going out in the town, on the streets, in the library sometimes, these can be a really big lifesaver. The great thing about them is that they are not electronic but you can adjust the amount of noise reduction that you get from them.  Quite often we want to get that noise reduction but we still want to be able to hear people so if someone comes up to you or someone starts conversation you can just flick it up and you can hear them. If you're interested in having a look what D-BUD is all about you can find the link down in the description.


Bonjour tout le monde, vous êtes les bienvenue chers amis de l'Institut. I suppose it's true to say that this session of the institute could well and aptly be called thinking the unthinkable, the unthinkable being of course for most of us anyway the election of Marine Le Pen to be president of France. What worries me basically is the number of people in France who have said to me that this is impossible. Where did we hear that before and quite recently and in my case including from the American ambassador so in the case of the US elections. It's true that in 2002 Marine's very controversial father Jean-Marie was stopped from becoming president by the Socialist Party joining forces with the right in the second round to elect the goldist Jacques Chirac and it was Lionel Jospin who had been the Socialist candidate. However we have a very different France 14 years on and this has been a very different campaign. We have a France still defiant of course still a great republic but demoralized after a cancunna five years of Socialist rule under one of the most unpopular presidents I think it's true to say the most sorry the most unpopular president ever and the first not to run for a second term. France unlike its neighbour Germany which it which it watches with not a small amount of envy I have to say haven't been there recently has suffered much from the recent recession and is only now beginning to come out of it but still with high unemployment particularly amongst the young in agriculture which is such a central part of French life not particularly an economic center that as well but a cultural center historical center very most French people very emotionally involved and there is devastation in the countryside and in villages and a high result resulting in a high level of suicides among agriculture. There is a growing disaffection with Europe not only in France of course as being the cause of or at least some of its sales. There is an urgent need to reform French public life as we all know and perhaps in France a willingness but not among those who would be the most defected mainly public servants. There is a large wave of the French people who are now switched off from traditional politics particularly on the left who feel abandoned and have now turned towards the front national as being their last and only hope. There is huge disaffection of course also on the right who see life in France changing beyond what they are prepared to put up with and I'm talking I suppose mainly about immigration and of course there is terrorism and the ongoing threat thereof and of course how can anyone anyone forget we all forget of course by times but how can any person in France forget Paris the attempt the attack at Tante or Nice and even the less spectacular horrors such as the elderly priest dying in a small village by having his throat cut on his own the church of his own altar of his the altar of his own church. But a few months ago the forever resilient French Republic was settling down for the upcoming election campaign and a change of president which most people even on the left were surely fairly sure that it would come from the right. François Fillon presented a radical plan for reform a right-wing plan but nevertheless a plan that people found credible and Fillon had survived his years as Sarkozy's Prime Minister relatively relatively unscathed I think would be the term. And that is a feat in itself. And a practice in Catholic he had the he had the credentials and the what the French call probably l'allure présidentielle the they look for l'allure the aura aura thank you. So he had a lot of things going for him and he was he was the one who was expected to win certainly hands down against Marine Le Pen and he was a safe pair of hands and then disaster struck and it struck in a strange way it struck through on the pages the front pages of this of this magazine of this newspaper which many of you will be familiar with the Canare en Chine in all three of these that I happen to have I locks in them off afterwards if you like but they and the the accusations and the proof indeed of how he had defrauded the state totally changed things and we don't know how much it has changed things but today we will we have two eminent observers of France and to talk to us more about the campaign and especially what might be the denouement of this the denouement of all this which has in clear incredible serious implications for the French people in the first place of course but also for the people of Europe and that I don't think is an exaggeration and I'm sure John will talk about that so without further ado I'm going to introduce Laura who no stranger to the Institute I'm sure you've you've heard of before some of you and she's been a journalist for 30 odd years she's worked in Washington, Paris obviously and the Middle East she is an incredibly I've said this before about an incredibly versatile journalist because she can turn her hand to any story as far as I can see and judge but she has been reporting assiduously and particularly well on this French campaign she's obviously she has had several awards in her time and of course one of them being the lesion donna so Laura with that in mind we allow you to have the floor thank you the last time that I spoke to the Institute in the beginning of November 2014 I fear I told you that Anna Juppé would be the next president of France so I got it wrong but everybody else got it wrong too he was just flowing in the polls he was very very high and no one just imagined how Juppé could could be defeated what has happened is what Jean-Luc Mélenchon one of the four leading finalists calls dégagisme which dégagé means get lost basically and he took the term from the Tunisians who overthrew Ben Ali in Tunisia and in the last sort of six months or so we've seen circles we basically everyone thought that everyone feared that this presidential election would be a contest between Nicolas Sarkozy and François Fillon that was the sort of worst-case scenarios we would have a replay of the 2012 election well Sarkozy came in third as you know and in les républicains primary and Hollande was so unpopular he had to pull out in December but other people have been victims of this dégagisme as well Juppé because he's too associated with the past and even Manuel Valls who was a great hope of many socialists of the sort of more liberal centrist Jacques De Laure style socialist so all of these people been eliminated and in fact the two of the most powerful arguments against leading candidates are against Fillon because he was prime minister for five years under Sarkozy so he's associated with the past and with the establishment and even Macron who was an advisor economic advisor to Hollande and then of course economy minister under Hollande nothing is certain I thought a very good analogy which I got from a pollster on Friday was that he said in the past a French presidential election campaign was a bit like a Mondrian painting you know it was all lines and boxes and you could sort of analyze it and figure it out and he says this is a Jackson Pollock painting paint just squirted everywhere and so for in recent weeks we've kind of settled into the idea that the that Marine Le Pen will be will come in first probably in the first round on April 23rd and Emmanuel Macron will come in second and then they would go on to the runoff on May 7th and Macron would just wipe out Marine Le Pen and he would with the polls show he would win by 60% to 40% approximately for Le Pen but in the last two or three days one thing the most significant thing I think that's happened is Jean-Luc Mélenchon who's very far left has overtaken François Fillon he's now in third place and he has real momentum he's siphoning off all the votes that we're going to Benoît Amon the socialist candidate so he and Fillon are well Fillon is at 17% in a poll published today Mélenchon 18% but within they're almost within reaching distance of Macron and Le Pen who are tied at 24% so anything could happen and there could be a terrorist attack there could be another big scandal a sexual scandal or a financial scandal it's really impossible to predict what is going to happen so it's a four-way race I think I'll just sort of go through very briefly in order of probability okay if we're just based on the polls at present the most probable next president of France is Emmanuel Macron who as you know is only 39 years old that would make him the youngest ever president of France he has a lot going for him he's young he's good-looking he has charisma he has a very unusual love story which I'm sure you all know about his wife Brigitte is 24 years older than him she was his drama teacher when he was 16 years old he fell in love with her eventually she was married with three children his parents did everything they could to thwart this romance sent him to Paris to live with his grandmother and love triumphed and he married Brigitte in 2007 and she's very much present in the wings of his campaign Macron has a very broad appeal he basically appeals to the more centrist socialist the center and the more centrist right-wing people for example a lot of Alan Juppey's voters went to Macron after Juppey was excluded he his emphasis on renewal of the political class and on bringing civil society into politics getting rid of career politicians is also very popular the factors that are working against him are that his base is as of support is very undecided about half of his supporters say they're not certain that they would vote for him in the second round and that's compared to Le Pen who's got 86% of her voters say they will vote for her come hell or high water no matter what and Fionne has also pretty strong base about 70% of his supporters they're certain to to vote for him another factor working against Macron is his association with Francois Hollande who you know as Joe said is the most unpopular president ever in France and he was very much an Oland protege I know for a fact that Hollande is secretly hoping or not that secretly it's good the word has got round but he's he's hoping that Macron will win he sees Macron as his sort of spiritual son and Macron does not want Hollande's public support it could be the kiss of death for him the the Fionne camp has taken to calling referring to Macron as Emmanuel Hollande which is very damaging now another another problem for Macron is his he's actually is I hate to say minus the good looks and the and the charm he's very much like Hollande in his rhetoric in France do the on Thursday night had a big interview with them and they put together a montage of him saying en même temps you know at the same time to say well you know it is a beautiful day in Dublin today but en même temps at the same time we have rain clouds you know and he does this constantly and for example his response to the the chemical attack in Syria he said we must bachar al-Assad must no longer be in the game but I don't agree with the people who want to eradicate him what does that mean he can't be involved but you don't want to eradicate him on every problem that comes up every issue Macron has the on the one hand on the other hand and this drives people crazy and Marine Le Pen in the in the first debate said just listen to him talking and said you've been speaking for seven minutes and I couldn't possibly summarize what you've said she said you know you say a little of this and a little of that and and it's just is meaningless and that was quite damaging and then actually in the second debate there was a similar comment by a sort of one of these maverick candidates a little candidate called a silly no who said but you always agree with everybody you know and this was this was Hollande's legacy Hollande was the l'homme de la Sainte as the man who always brings arrives at a consensus not tell you held the Socialist Party together so people are a little bit leery of this this side of Macron that is kind of flu kind of blurry kind of all over the place and that that is hurting him he on his policies I think I think the most important policy of Macron is his pro-Europeanism he is the only real defender of Europe in this campaign he loves Europe he says I love Europe and he wants and I thought I was kind of surprised because I thought it had totally gone out of fashion it was finished forever he wants European integration he wants a federal Europe he wants to create more European institutions he wants countries to to give up he's against intergovernmental Europe he says it's dead and that's his biggest difference with fion because fion wants the continuation of these summits of heads and state who turn out the the the communiques and so on he also wants to relaunch the Franco-German engine he wants to reform the labor market actually most of his policies are quite similar to fion's policies but I would call them fion light you know instead of firing 500,000 civil servants he wants to get rid of 120,000 civil servants instead of fion wants to abolish the wealth tax the ESF imposter la fortune and Macron says well I'll abolish it on stocks and bonds and shares and capital investments but I won't but I'll keep it on property there's another example of him doing always balancing he always does the balancing act and I was very struck I'm reading two two books right now one about Montroteau which is the family Chateau of the Le Pen's which is actually very amusing and and a more serious book by Macron which was called Revolution which is his sort of manifesto and it's very interesting the character difference he and Marine Le Pen who are likely again to be adversaries are complete opposites because Le Pen goes on about how she's persecuted how everyone is out to get her she's a victim and so on so and when you read Macron's book for example he never mentions that his parents tried to keep him away from Brigitte and sent him to be raised by his grandmother it's just everyone is wonderful in his book he likes everyone he's very he's nice as the French say he's smooth you know and and he's he's positive he's cheerful pollsters say that the most important determinant between Macron and Le Pen voters is whether you're an optimist or pessimist and if you're an optimist you vote for Macron and if you're a pessimist you vote for Marine Le Pen and this is even more it's also a social class vote because the more educated affluent people tend to are voting for Macron in fact and that's something else that hurts him he's seen as the candidate of the bankers the candidate of the elite and I hear people say all the time I say the selam des banquiers you know and that that that hurts in France but if I had a pollster told me on Friday if you're a working-class person you would probably vote for Le Pen except if you're an optimistic worker in which case you would vote for Macron and if you're affluent if you're earning a good living you should be voting for Macron unless you are very pessimistic if you think the country is going to the dogs and that is being destroyed by immigration and Islam you will vote for Le Pen despite the fact that you're affluent so I thought that was very very telling I'll go very quickly I mean he's at 17% he looks and acts sounds presidential you when you hear him speak you think wow you know he he's imposing he's playing on experience also on the thirst on the French right for what they call alternance and I'm not really satisfied with any translations I found of alternance it's a change but it's it's also it's you know the in France we've had left right left right left right and and they alternate in power and the right feels it's their turn to come back to power and what's happening and I can see it around me as a lot of them are disgusted with fion and they're there with good reason because of the fake job scandal and the free suits for 50,000 euros and so on they think he's a crook but they say if it's if it's him or a continuation of Oland or Marine Le Pen we're gonna vote for for fion and I and I know people who had decided not to vote for fion who've now have come back to the idea because they like his economic policies because they think the country really needs to be reformed and shaken up so that could it's not impossible that that he could could win still and it would be by the way this election will be the first what we will have had not one but two candidates who are mise en examin which is basically you've been charged with corruption both fion and Marine Le Pen although she refuses to answer the judges summons two of the four leading candidates and fion's wife Penelope as well fion is compared to Margaret Thatcher and he seems to be several decades behind the times but he he really wants to to enforce rigor and austerity and totally reform the economy Melanchon who's made this amazing breakthrough his party is called La France Insoumise France Unbowed he's a trotskist he's changed a lot since the last campaign when he I was studying your colleagues over lunch in in 2012 he admired Hugo Chavez and now he talks about General de Gaulle in 2012 he played the international at his rallies and now he plays the Marseillaise and so on he has a chance as well if Melanchon wins he wants a 100 billion euro investment plan he's going to borrow to finance it and he wants to 173 billion euro in new expenditure so basically fion and Macron want to reform the economy they want to save money and Le Pen and Melanchon both want to spend a lot Melanchon by the way had a rally in the port of Marseille last night he had 70,000 people show up so he's really the the man who's got the momentum at the moment is Melanchon Marine Le Pen I can't not say something about Marine Le Pen it's a paradox that she is the most popular the the full national is actually the the leading party by all opinion polls she's getting 25 30% in elections in recent years but she will still lose because there's a sort of glass ceiling there's a deeply entrenched aversion to the extreme right and everyone believes that this will kick in and that she won't do more than she might get 40 she might get do better than expected maybe 45% in the second round but very few people believe that she will actually win although we're all nervous working against her or is this this feeling also she made an unfortunate comment yesterday she said the French were not responsible for the Valdives you know the big roundup of Jews in Paris during the Second World War which Shearac was the first president to admit that this this had been the fault of the French and then she kind of did a lot of back peddling and said no she said I believe like General de Gaulle believed that the true France was in London with General de Gaulle and the people who did that were not really French and so on so she's she's back peddling but that's that's hurt her and and it also reminds people of the sort of neo fascist foundation of the Front National and also the fact that she has she has neo-nazis in her immediate close circle guys like Frédéric Chatillon who used to celebrate Hitler's birthday and send condolence notes to each other on you know the anniversary of his death and I've been surprised that hasn't got too much attention so far but this her remark about the Valdives could she also has problems with her old father who every once in a while makes an unfortunate remark and with her niece who is a competitor really who's who's very attractive who's further to the right for Marine and if the Front National doesn't win this time they could win in 2022 and it could be Marion Marion Marion Le Pen instead of Marine and as you know Marines policies on I think if she's elected it's it's the end of Europe as we know it I just don't see how Europe could survive the she wants to leave the Euro you know all that a few very few points and I'll summarize here the fact that three out of four leading candidates are pro-Russian close to Putin Mélenchon Fillon and Le Pen all prefer Russia to the United States well with Donald Trump in office that's poor Emmanuel Macron is sort of left out on a branch because he is pro-American but he doesn't have any affinity for Donald Trump and then another question is the future of the Socialist Party if neither makes it to the runoff the two French political families that have ruled since the Second World War it will be more or less finished the big question about the legislative elections if if Le Pen or Macron or probably Macron wins in June can they get a parliamentary majority and to carry out their programs John Brennan is Jean Monnet professor of European integration in Ménouth he's obviously published a lot on Europe and integration has spoken in many places including in the Institute I think here and he now joins us to talk about the implications of all of this and I think they are serious from what Joe gathered for Europe and where Europe I suppose is going in any case yes well thank you very much indeed Joe and thank you for the invitation and it's a pleasure to share my speaking time with Lara whose work I've been reading like now you view for a very long time now I just have 10 minutes or so so I just want to throw out I think what might be the most important issues that link the French presidential election with European integration in general I think it goes without saying that the French presidential election is truly existential for the EU where Brexit is a minor irritation it's going to absorb a lot of negotiating energy and time perhaps over the next few years unless the talks break down very quickly which they might but the prospect of a Brexit is truly existential because it pretends the disintegration of the European Union and whereas I think up till recently the chief concern in Brussels and the national capitals was about obviously the prospect of a Le Pen victory the extraordinary momentum behind Mr. Melanchon over the last week I think is also giving Brussels pause for thought and the nightmare prospect I think is of a runoff between Le Pen and Melanchon and as Lara said given where they are respectively in the polls and the crime that Melanchon has made after the last televised debate last week it is not out of the question that it could be Le Pen, Melanchon even if it seems much more likely at this point in time that it will be a runoff between Le Pen and Macron and obviously the European Union is has a real interest in this result it was extraordinary I think to see the European Commission enter the fray in the French general election by setting up a fact-checking operation that was deliberately put in place to try and counter some of the lies mischaracterizations and so on that Le Pen campaign was was using was deploying now this election I think comes at a critical time obviously because it's one of a series of hugely important elections in Europe this year that will culminate with the German election in September with all due apologies to the Czech ambassador the Czech election comes a little bit later but it is of course the French and German elections that will decide the future direction of the integration process now I think however we should pay attention to some of the most recent electoral contest that we have seen where the evidence suggests that the wave of populism that has engulfed Europe in recent years may well have peaked you may remember in the Austrian presidential election the far right candidate was defeated when it was expected that he could well win in Bulgaria most recently the center parties that is Gerb on the center right and the Socialist Party on the left emerged as the two largest parties and Gerb will most likely form a centrist coalition of some kind in the Netherlands Geert Wilders seemingly unstoppable rise to the top was halted and it looks like he has very little prospect of being part of a coalition and in Germany I think it's worth noting also that Mrs Merkel who seemed to have fallen behind the SBD once Martin Schulz emerges as her challenger actually did very well in the Saarland lander election last Sunday week and again there the interesting comparison can be made between the AFD the alternative for Germany and the front national both of those parties seem to be flatlining in terms of their overall level of support over the last six months where they had seemed to have real momentum over the previous year to 18 months so I think there is a view in Europe a sense of quiet expectation if you like rather than trepidation which was there previously but I think the worrying thing in the French case which Lara has pointed to is the potential collapse in the centrist vote if for example Les Republicans and the Socialists if you put their combined votes together and they dropped to third and fourth or fourth and fifth place which is very conceivable in this election then we will have further evidence of the collapse in the centrist vote in these hugely important jurisdictions in Europe but of course that's contradicted by the fact that Macron is a centrist and if he wins it will be a triumph for the center as much as anything else. Secondly just to speak briefly about the French parliamentary elections which will follow the presidential elections in June I think these are going to be absolutely critical because if Macron wins it will inevitably be some form of cohabitation regime that emerges. His movement the Amarche movement is a very new one a lot of his candidates come from outside of mainstream or traditional party politics it's very difficult to say how they will actually do how many seats they will win in parliament but it's very likely that he's going to have to assemble some form of coalition in order to be able to govern so will we get an operable coalition in France from the European perspective instability would be certainly not as bad as a win for Le Pen or Melon Chong but it's something that will complicate I think the effort to repurpose European integration at the same time remember as the Brexit talks are ongoing. So I think the clear desire in most European capitals obviously is for a win for Emmanuel Macron and that opens up possibilities as Lara said Macron is for all the wishy washyness there is a commitment to European integration and federalism which is rather a novelty these days in centrist politicians of the right or left federalism is one of those things that is just not embraced in the same way that it was previously in different European jurisdictions but I just wanted to take up Lara's point about Macron's opposition to intergovernmentalism there I think we might run into a problem because if Mrs. Merkel is reelected and she remains as committed to what she calls the union method of governing which is to privilege what goes on in the Council and the European Council and Macron's idea of Europe is very different in that he embraces a strong role for the Commission and the traditional community supernational structures then I think we could be running into a potential conflict between France and Germany however it is much more likely that we're going to see the reanimation or the repurposing if you like of the entire European integration process there's a space if you like potentially opening up that hasn't been there probably since the 1980s that will allow for a rethinking of the European Union a rethinking of its institutions and its purpose in the early 21st century are we likely to see a big leap forward what kind of initiatives might emerge well I know there is some hope to attach to the view that a progressive alliance might be possible that if Mr. Macron wins in France and if Martin Schultz were to trump so to speak Merkel in Germany in September that will allow for a rethinking of European integration along progressive lines and this would be very different to the kind of Blair right approach to Europe. Macron of course is compared very often to Blair for very obvious reasons but Blair was never a federalist he was never really much of a supporter of the European integration process so I think it's probably more likely that we would see a progressive kind of Europe emerge if it's Macron Schultz but even if Mrs. Merkel is reelected I think she's going to have more space in which to be able to operate than she has had over the last number of years I think we're going to see a very different approach to Turkey for example in time and given Brexit and given the election of Donald Trump I'm sure that in her mind the reanimation of the integration process is hugely important now what form might that take well as yet it's very unclear I've been struck in the aftermath of the publication of the recent white paper of just how similar it is to all of the debates that were going on during and around the constitutional convention in 2005 all of that argument about variable geometry about the potential move to a core Europe and that France and Germany of course would lead that and that that in itself would act as a catalyzing force all those things are back in play of course but the one thing is missing I think is any focus on deprivation on welfare on the extraordinary problems in southern Europe in particular of unemployment and the link between that and the delegitimation of the European integration process I see no evidence that in Brussels that that has been taken seriously enough and finally just to deal with Russia because Lara mentioned that three of the four main candidates are in one way or another what might be perceived as pro Russian that will present a problem I think because the European the European Union's regime of sanctions against the Russian Federation has been difficult enough to preserve in the aftermath of the election of Trump in the United States I think it's going to be more difficult notwithstanding all of the difficulties and that he faces in Washington about those relations but I think that's definitely a space to watch that the relationship between Russia and the European Union to some extent is a prisoner of the Russian Washington relationship of course and but obviously again here the election of Macron would make things much easier for a coordinated European response to be maintained than if Melanchon Le Pen or Fiong was to win thank you very much indeed