I’m not attending CHI in person this year. I had arguments with people about it in 2020 before CHI got canceled as the pandemic’s seriousness became more apparent, and I expect to get dragged a bit by the same people for being annoying or a “killjoy” or something all over again. One difference between then and now is that I’ve read more of Sara Ahmed’s work, so I’ve gained an appreciation for being called a “killjoy”. Another difference is that I’m happy to outline some of the things that I’m thinking about when I think about attending an event in person. So I’ll write them out here, and if it’s at all helpful to point to this, then please do.

One of the things I’m thinking about for 2022 is the CADE Speaker Series. I thoroughly enjoyed the 2021 speaker series, and I’d be happy to do more of that, but I also want to reflect on what should (or can) change, and whether that would help or make the whole experience better. If you’re not super interested in event organization and logistics, you might absolutely will find this post unbearably tedious, but if you invite speakers to events and/or if you’re interested in this stuff, you might be able to tolerate it. In particular, if you’re a person who gives talks, I’d love to hear your thoughts.

As part of the application for the Data Ethics Fellowship, we ask that applicants write a statement reflecting on diversity, equity, and inclusion, and on supporting Black and Indigenous people of color. USF asked for it and I didn’t object because my issues with diversity statements aren’t in the statements themselves, but in the measures they displace. That’s a little vague, so I’ll try to ground it: I think when institutions like CADE ask for diversity statements, we tend to overlook our responsibility to make meaningful commitments to justice, and imply that that’s your job. I want any applicants to be able to find some public thoughts from the person they might be working for/with on the topic of justice, equity, diversity, and inclusion. So I’m offering some of my thoughts here.

I wrote about this a few weeks ago and got some great feedback and questions, and in that time the call for applicants has made its way through USF’s system and is now up and going, so I can officially say that I’m looking to bring 3 people on as Data Ethics Fellows at the Center for Applied Data Ethics at the University of San Francisco. All applications received by November 19 will receive full consideration, but I’ll be reviewing applications and scheduling interviews as soon as I can. More info in the coming paragraphs (or after the fold).

On Friday I briefly talked about a call for data ethics fellows that’ll be coming out soon, promising more information on Monday. well, it’s Monday Wednesday Friday. My bad. At the bottom of this page is a link to the draft call for applicants as it exists today, but I wanted to make as much of the info behind what we were thinking available as I could, and I want to hear feedback if there are parts of this that are unclear or ambiguous, and similarly if there are questions that I can answer or incorporate into the final version.

I’m not sure exactly what I’m waiting for (I mean, I know why I’m waiting, but that’s another post), but in the meantime between now and when I make the paper more publicly available, I’d like to unpack a few things that I didn’t find time to talk about in the paper itself. This is a weird experience for me to have a lot that I wished I could have talked about so soon after wrapping up a paper (usually I’m so wiped out that I just need a break), but I’ll take it as a good, or at least generative, sign.

I’m between drafts and projects and this seems like something worth unpacking further, but I haven’t come across someone else’s work on it, so I figured I’d put it here. If you’re too lazy to click, the gist is that there’s an epistemological rift in AI/ethics. Specifically, what we teach people they need to be knowledgeable and authoritative on ethics and AI differs based on where we’re coming from intellectually, and I think it’d be helpful to talk about it.

There’s a lot to unpack about Jeff Dean firing Timnit Gebru after what appears to have been a substantial amount of time trying to find excuses to push her out of the company, and failing to do so because she was very annoyingly very good at her job, managing a team that seemed to like Dr. Gebru a lot and that collectively consistently published important work. I’ve never worked at Google and I can’t comment on how awesome her team was internally, but I can unpack part of what’s shaken the foundation for me with regard to a point I made on Twitter a while ago - that now I don’t know how I can situate the work I read that comes out of Google’s Research arm, and specifically Google Brain. I don’t even think I can come up with a model for how Jeff Dean probably wants me to read the work that comes out of Brain, which is my way of saying that I’m completely lost.

I gave a short talk recently about some of the stuff I’ve been working on (I’ll share the PDF/video sometime soon). After the talk, someone asked a question that, since I didn’t copy the question down to preserve their anonymity, I’ll summarize/restate a bit: Given the issues with metrics describing our lives, should we deal with the inherent shortcomings of algorithmic systems, or should we work on developing more metrics and measures? or a bit of both?

A few months ago I tried this “coffee talk” thing through June. I met with a bunch of people and I think we had a lot of good conversations; I’ll let them speak to that, but I felt like I got a lot out of them. There were some bits that didn’t work, so I’m changing the format a bit, but the gist is that I’m continuing it, and you can book a chat here, but if you’re at all curious about how this format will look, read on.

A handful of people have emailed or variously asked if I could chat, and in the interest of trying to be available beyond the insulated network of “people I either already know or who have a mutual friend to introduce us”, I wanted to try opening up chunks of my schedule to what I’ll call “coffee talk”. You can sign up here (but you might want to read on to understand where I’m coming from so your expectations are measured).

Over the past few weeks, Apple & Google have floated the idea of developing and distributing a digital contact-tracing app that will inform people when they’ve been exposed to someone who’s contracted COVID-19, and communicate to people that they’ve been exposed to you if you later test positive yourself (edit: since writing this, Apple has released a beta of iOS 13 that includes the SDK necessary to begin using this system). Writing this in late April and early May, it feels like we’re desperate for information and weary from not knowing who’s caught COVID-19, who’s still vulnerable, who gets it worse or why, or even how to treat it. We’re desperate for any information we can get our hands on. This proposal by Apple and Google promises to give us some information that we can finally start to work off of. This isn’t going to work, and we need to stop this plan before it gets off the ground. I’ll explain why in this post.

One of the busier intersections in the study of algorithmic bias has been in automatic judgments along race and gender, but decisions about how to frame and present information to people making decisions are immensely important, and from what I understand it seems that the evidence from studies isn’t consistent about whether something like a photo of the person involved in a given context helps or harms them. I’ve also been thinking about what the human component of AI-training systems might look like (for people evaluating appeals, or for people doing labeling tasks, for instance), and this paper seemed to approach that topic pretty directly. In this paper - “Do I Look Like a Criminal? Examining how Race Presentation Impacts Human Judgement of Recidivism” by Keri Mallari, Kori Inkpen, Paul Johns, Sarah Tan, Divya Ramesh, and Ece Kamar - Mallari et al. replicate an important study (and I think come up with problematizing findings, but offer some context to help it make sense), and go further by thinking about what this might mean when we design systems.

In statistical terms, “significant” approximately means “this probably didn’t happen by random chance”; in everyday language, “significant” means something more like “that’s a big deal”. Don’t speed past that sentence - it’s important. In academia we put untold numbers of hours into communicating our work to the people who have similar technical backgrounds to us - and use language in the same way - so when something we do pulls in huge numbers and garners public attention, we’re rarely well-prepared for it. I lost track of the number of times students and even professors expressed apprehension about journalists asking about their work when I was in grad school. There are unique, complicating factors in that case, but I think everyone’s anxious that they’ll say something and it’ll get misquoted, or quoted but out of context, or the audience of everyday intelligent people will read something like “significant” and take it in a direction we didn’t mean. One of the papers I read today - “Disseminating Research News in HCI: Perceived Hazards, How-To’s, and Opportunities for Innovation” by C. Estelle Smith, Eduardo Nevarez, and Haiyi Zhu - offers a sort of taxonomy of pitfalls, and how to try to avoid them.

It’s April 2020. There’s a pandemic sweeping across the world, and many of us are basically locked in our homes reading the news and consuming visualized data about infection rates, mortality rates, charts telling us what trajectories we’re on in comparison to other countries around the world, and trying to make sense of the magnitude of the trouble we’re all bracing ourselves for. One of the pernicious, kind of evergreen problems in HCI - or more accurately in data visualization - is how to communicate really out-of-scale data in a way that doesn’t totally lose the person trying to grok what’s going on. That’s sort of the whole point of data viz, after all, isn’t it? So this paper - “Du Bois Wrapped Bar Chart: Visualizing Categorical Data with Disproportionate Values” by Alireza Karduni, Ryan Wesslen, Isaac Cho, and Wenwen Dou - was really timely; this paper offers a historically inspired data visualization tool that (at least in some situations) helps people juxtapose bits of data with wildly different values in a way that doesn’t totally lose all meaning.

I alluded to this in the last post, but I’ll be trying to write a bit about each paper I tagged as interesting in the list of CHI papers that caught my attention. If you’re one of the authors in one of the papers I listed, and I haven’t linked to it (or if you’d just like to chat about it), please reach out! My goal isn’t to give a deep reading to papers. If you’re a first-author and you’d like to do a deep-dive conversation, I’d love to try that (maybe we could video chat, or just DM on twitter, or something), but my goal is to get across what sparked my interest in the paper. If you’re new-ish to the HCI space and you see a title and you’re thinking “why did Ali gravitate toward this paper?”, then hopefully these posts will help give some insight into that. My thinking is that you might have read a title, thought “that doesn’t sound like my thing”, and passed on it; if my blurb sparks your curiosity enough to go check out the paper, then I’ve succeeded. That being said, if you read my blurb and you’re like “okay, got it, but that’s still not my jam”, then that’s totally fine.

Academia is a difficult line of work to “succeed” in, where success is often defined as being noticed in a noisy field where people work for long hours in obscurity, sometimes feeling like you only surface to get smacked in the face with harsh rejections. Conferences are a rare opportunity to be up on a stage talking about something you did that other people collectively decided the field should be proud of. So it feels extra painful to imagine early career researchers who were looking forward to CHI, hoping for a rare opportunity to share their work and perhaps make themselves known to people they respect, only to have that opportunity taken away by a global pandemic.

I’ve been reading about trees and forestry lately, and something about this story James Scott tells in his book Seeing Like a State has me thinking a lot about online advertising (and, more generally, the design of sociotechnical systems). It’s a story about the exploitation of a complex resource and the delayed, but ultimately devastating, toll that this exploitation took. But more substantively it’s about the reductive functions we use to make sense of the world, and how those functions become dangerous when we let them run our lives.

Last week Stanford launched the institute for human-centered artificial intelligence, and to kick things off James Landay posted about the roles AI could play in society, and the importance of exploring smart interfaces.

I’ll try to keep this brief because every time I start writing a post I get mired in the details and eventually think “lol who cares? why am I even blogging?” - This year has been good, I think.

I was talking to a friend about a feature that would be kind of neat for the HCI group’s site, and it came up that Google Scholar doesn’t recognize her name since it’s one letter. Let’s set aside the point about Google Scholar being stupid for assuming such a dumb thing about names and how they should be getting input from non-white people, non-male people, etc… and talk solutions. Well, sort of solutions.

I’ve been shopping this idea around to some people because I don’t want to build it myself.

This is sort of a marginal update, but recently I was a little annoyed that there wasn’t some obvious place to import all of the CHI papers into a single bibtex file. So I went and downloaded them.

About a week ago I gave a presentation at CHI (‘17) on the paper I wrote on crowd work and gig work and piecework. I mentioned that you can get the link from my profile on Twitter (via this link), but a blog post is a much nicer way to point to stuff because I can give a few options:

Along with another PhD student (in Communications) and his advisor, my advisor and I recently submitted a proposal for funding to study professional YouTubers, streamers, and other online performers given the growing interest (and drama) in these communities. I think there’s an interesting perspective in there related to street performances. Funnily enough, in a book I’ve been reading (Drawing a Circle in the Square), there have been some funny quotations that are a little uncanny that I figure I’ll share

You probably could have guessed that Stanford has a program allowing undergraduates to work on research projects over the summer. It’s called CURIS, but if you’re familiar with “UROP” at other universities, you’ve got the idea. What you may not have guessed is that Stanford has a program specifically for non-Stanford applicants, called the CSLI Summer Internship Program.

Earlier today I gave a talk to people affiliated with the Hasso Design Thinking program. My talk was short and basically just covered the piecework paper coming up at CHI. You can find the presentation here (pdf) or here (tex) — with the tex file you’ll probably want the figures directory as well (check the github repo if you really want the whole picture).

This is way off topic, so I’m sorry if you’re seriously disappointed after reading this post.

Some things on my mind:

Lately I’ve been thinking about the ways people make money on the internet. I’ve spent some time thinking about people on Amazon Mechanical Turk, as well as on sites of work like Uber, Lyft, etc… but there are other platforms online where people work under different conditions, and I’m always looking for something more fringe, less explainable by the research we have, and just more interesting. I guess this is a long–winded way of justifying watching lots of YouTube videos lately.

I submitted a paper to a conference a few weeks ago (you can figure out the details if you want to dig through my github repo, but I’m reluctant to point a flashlight at it until the review process has run its course). Since the submission, I’ve mostly been thinking about what larger arc of research I want to try to explore from here. It’d be nice to keep my head above water just by getting any papers published, but I get the feeling that if my next move can be informed by some intuition about the big picture, then the narrative thread of my story will be that much stronger at the end, when I tell it retrospectively (read: when I’m working on my dissertation).

A few weeks ago my mentors from my time at MSR/FUSE Labs back in 2015 published a draft of suggestions for anyone interested in designing a peer to peer labor market. You can find the paper here. In it, we make a number of suggestions that I think would be important for anyone who wanted to make these markets more worker-centric. You know. Hypothetically.

If you were at the CSCW Workshop on The Future of Platforms as Sites of Work, Collaboration and Trust and you’re wondering if you can have my slides, take a look here (pdf).

I got the notice that I’d been accepted to participate in a workshop a while ago, so this is somewhat late (sorry!), but if you’re interested in reading my position paper on “gig work”, please take a look here.

Last night someone asked a really great question in response to the fuse labs post I wrote about the worker-centric market that I’m working on. The question (again, see the link earlier) was this:

I’ve been having some really interesting conversations with fellow MSR interns this summer. One that stands out recently is a chat I had with Rhema Linder who mentioned that a dump of Geocities is available to anyone who doesn’t mind torrenting something like 1TB of data.

In the 1700s a man named Wolfgang von Kempelen invented a machine seemingly capable of beating any human at chess. If your memory is really good, you might know offhand that it took IBM until 1996 (or arguably maybe 1997) to recreate that accomplishment with something called “Deep Blue”. Obviously Kempelen didn’t construct a computer the way IBM did. Instead, Kempelen constructed a box which gave the illusion of calculating potential moves, but in reality housed a person. So effective was this illusion that Kempelen traveled the world demonstrating the Automaton Chess-player to the likes of Benjamin Franklin and Napoleon Bonaparte. You may know this machine simply as “The Turk” - a reference to the false humanoid automaton which seemingly made each move.

It’s been a surprisingly long time (not really)! Much has happened (this is true)!

This summer I’m interning at Microsoft Research (in the FUSE Labs, which as an aside is an awesome place that you should definitely try to get into if you have the opportunity). I can’t discuss the nature of the work in very much detail, but hopefully it suffices to say that it’s in the same space as some of my past work. I’ll get to talk more about what I’m working on later, and I’ll (almost certainly) be writing a paper or two based on this work, so hang tight for about a year or so.

I’ve been thinking about (and working on!) this post for the past few weeks, particularly after I started seeing the graduation photos of friends who walked this year. 1 year (and 1 day) ago I walked across the stage at the Bren Events Center at UC Irvine, recognizing my having earned a B.A. in Anthropology. It meant a lot more for me than 4 years of hard work, though.

It’s been a while since my last post (I keep saying that, reducing the value of my apologies, but I’m going to keep apologizing anyway). My bad, sorry. I was working toward a CSCW deadline until about a week ago I realized that the paper I was writing was never going to go to CSCW (or if it would, it would get so soundly rejected that it wouldn’t have been clear whether I would have learned anything from the first round of feedback). Before I made this realization, I made some observations that I wanted to jot down and get back to later, and it’s sort of twofold:

This quarter the HCI and Infolab groups came together for a weekly reading group thing. Each Monday at noon someone brings a new, impactful reading to the group and discusses it for everyone. It’s kind of a cool way to get the HCI group - often somewhat ethnographic or qualitative-leaning - and the Infolab - generally more quantitative - together to see where cross-pollination of ideas might happen. We’re tentatively calling it “Reading With Friends”, but I’m pretty sure we’re open to alternative names.

Quantified Self, Azumio, Argus, etc… Talk

I got a chance to give a talk at my old community college for my Anthropology professor/mentor Sam Connell. I’m pretty sure he knows that if he asked me to give a talk in Nebraska to an empty room I would begrudgingly pack my bags and do it, but fortunately he hasn’t asked me to do anything that hasn’t turned out to be awesome.

Recently I did something rude and hogged a compute server of ~96 cores. Unsurprisingly, I got an email from someone in the lab asking if I could do something about it. At first I assumed I’d have to kill the tasks that were running on the cores he needed, but then I realized Linux would probably just reassign other tasks into those newly-freed cores (a feature in most cases, but in this one instance a bit of a nuisance).

One might file this under “posts I wrote while waiting for a huge task to finish”.

Recently I was asked to talk about a paper to the infolab group, and I couldn’t help but choose an Anthro paper. Here are the very low-quality notes that I prepared for the paper I gave everyone (“The Anthropology of Online Communities” (contact me if you can’t access JSTOR)).

This post got away from me, so I’ll try and roadmap it:

Recently the team I’ve been working with got a response that mentioned revisions to the Common Rule, a guideline which informs ethical concerns in research. Consequently, it typically has pretty far-reaching implications in research protocol approval by the IRB.

A few days ago Jawbone announced a new revision of their activity tracker called the UP. This one is called the UP3 and as you can probably imagine, it’s their third generation device. I used their second generation device - the UP24 - for a number of months while studying the culture of Quantified Self (at this point linked enough times that I’ll just say “google it” if you want to read it), and I think that from that perspective I have some insight into the sorts of things they need to do to remain true to their QS roots, gain some budding self-quantifiers, and win precious wrist-space from new and old competitors.

First, the part you probably care about (a regex pattern to identify Markdown files):

Jure Leskovec has been teaching CS 224W this quarter, and a number of his lectures have used research questions to illustrate the concepts and models that he’s been teaching. It’s been an interesting way of surveying the kind of research he does, which is hopefully informative for anyone planning on rotating into Jure’s lab (e.g. me). Recently, he talked about a question of predicting Wikipedia admin elections, and described the results of some research wherein he found that, by accounting for voter-candidate similarity and for status difference (again, between the voter and the candidate), you can predict the outcome of the election just by knowing which 5 people came out to vote first with ~70% reliability; this, without knowing for whom they voted. Knowing their votes, you can predict with approximately 85% accuracy. By looking at voters 1-10 (again, not the votes, just the turnout), accuracy rises to 75%, after which point accuracy rises negligibly - even with all voters.

Last week in CS 547 David Glowacki gave a talk about some technology that his lab developed which allowed people to use Kinect (and incidentally LeapMotion) to view the effect that their bodies - as sort of “force imprints” on a canvas (the background) - have on particles bouncing around arbitrarily. It was really interesting seeing a serious implementation of Kinect and LeapMotion that really seemed to have legs, since I’m generally kind of skeptical of the appeal of these full-body user interface devices (almost as much as I am about voice interfaces, although my opinion on that has been changing recently as I’ve biked to work/class and wanted to know things that only Siri could tell me).

Over the past several months I’ve been working on a project involving Amazon Mechanical Turk (henceforth AMT), a fascinating online labor market for “micro-labor”. Some researchers have done the whole crash course ethnographic thing, but for the most part research of online labor has taken a look from a step back (as Horton, Silberman et al., and others have done).

After getting some feedback from my advisor I’m pretty sure I’m ready to put this out there…

For the past year I’ve been studying the culture of Quantified Self. Last weekend I gave a talk on the research at the Undergraduate Research Opportunities Program Symposium, and in the spirit of QS culture (especially with regard to open access to data, etc…) I thought I’d post the presentation. The PowerPoint is here (warning: 12.9 MB). Alternatively, you can download a PDF here (9.8 MB).

My last substantive post was about how broken graduate school applications are, so I think it makes sense that my next post would be about graduate schools again.

Apparently there’s some sort of contest to see who can get the highest PageRank for “Flatricide pulgamitude”. Seems silly, but I’m curious to see if I have any PageRank after all.

When I started working on grad school apps I found myself using a lot of the same words, terms, phrases et al. pretty frequently. In some cases, it was justified (it’s awfully hard to get around the word “qualitative” and “quantitative”), but in other cases it was just me being lazy and repetitive (you can call “qualitative and quantitative methods” something else, like “mixed methods”).

This is one of those times that I should be focusing on something more important, but there’s always something more important that I should be doing (certainly more important than writing on a personal blog I’m maintaining for my own sanity and reflections).

About a week ago I deactivated my Facebook account. I went through the whole process of disabling all of the hapless shit Facebook does to keep me tied in (the periodic emails, the notification emails in case my friends say stuff I might care about, etc…) and entered the confirmation code, and that was that.

I was sorting through some work I did during Spring quarter and despite taking about twice as many units as anyone else I knew (and more in the case of a few people), I managed to churn out a few kind of interesting ideas.

I’ve been poring over this paper on the economic structures of various social networking sites. It’s actually really interesting how the reciprocal economy on Twitter (Follow Friday, Follow Back, etc…) contrasts with market economic systems like Quora’s credit system, Reddit’s upvotes, and others.

If I’m quiet over the next week it’s because my brain is mush from finals and I’m taking a break to deal with a personal issue with my flatmate.
Seems he got angry and trashed my bedroom on Thursday.
I’ll have more details at some point in the future.

I tried to be cool about it but let’s face it, I’m not a cool guy. I got a comment from Anil Menon and it’s kinda like a drug when someone whose work you’ve read suddenly interacts with you. I mean I know that authors and professors are people, it just… kinda strikes close to home when they pop in to comment on my blog.

This class was ridiculous. I continue to find that these geographically focused Anthro courses are miles away in terms of rigor compared to anything else I’m taking. I found myself thinking the same things as this quarter came to an end as I felt near the end of Anthro 164P (Eastern Europe and Central Asia):

I spent way too much time making this stupid lab look right (and don’t get me wrong, it looks great and accounts for nearly every stupid little thing someone could possibly think up), and I realized that I got a higher grade on a proposal I turned in for another class, meaning I actually could have gotten an A in it when before I thought it would take a miracle to accomplish.

With Cultural Anthropology going open access as per news yesterday, the world is one step closer to democratizing knowledge.

In a post on Techcrunch a few days ago Michael Arrington posted his epiphanous realization that posting content to Twitter that gets auto-posted to Facebook garners less feedback on Facebook.

I’ve been getting graded really harshly in these lab assignments lately and I’m beginning to feel worse about my ability to code and do well in classes and all of that.

I’ve been reading Machines as the Measure of Men lately, which details how the West asserted dominance over “savages” using a variety of means. It’s actually quite interesting to read parallels in historical practice and reflect on how some of those practices and perceptions still play out today. Indeed, the effects of western hegemony and perceptions of hierarchy still ripple throughout non-western civilization in myriad ways.

I’ve been getting too many ads from Hulu on depression.

About 4 weeks ago a feeling began to sink in that ate at me for another week and ultimately drove me to a pretty severe depression. This nagging, all-consuming voice made me feel small and worse yet, it said this was all my fault, and I knew it to be true.

Over the past several weeks I’ve been reading articles and books describing the relationship between Colonial Britain and India during the 19th and 20th centuries. In all of these readings, I’ve noticed a thread that seems to connect every anecdote and interaction between the West and the “other” from then until now: The “West”, assured of its own superiority and set in its views about what is “good” versus “bad”, imposed its binary paradigm on India in an effort both to pigeonhole Indian culture but also to reckon with the subtle, sometimes confusing, often contradictory aspects of Indian culture.

After looking at the blog, I realized I hadn’t posted a screenshot even vaguely resembling the current app. Here it is.

With the Fall quarter finally over, I find I actually have time to blog. Great. I also find that I have time to run more, clean up around the apartment, brood over my sloven flatmate, and run some more.

Good news: I’ve managed to pull the last tweets of people a user is following:

What’s the best way to do a Twitter clean-up of who you follow? Need to remove dups/junk accounts/dead accts, etc.

I’m a little more than a week into the Fall session at UC Irvine, and I’m struggling to find meaningful activities to fill my time. I’m taking 16 units, found a part time job, and am exploring Alpha Phi Omega (a community service fraternity) and Circle K International (also a community service organization, but this one a club), but I still don’t feel like there’s enough going on this quarter. I’ve reached out to the undergraduate research adviser in the Anthropology department, but it seems that the professors I would be interested in working with are all busy or away at the moment. I’ll need to wait until the Winter quarter to consider even exploring research with them.

Taking an introductory Anthro course (on ethnographic methods) has been surprisingly rewarding. ** Don’t get me wrong; most of the historical background and ideas of pluralism, multilinearism, etc… are all familiar to me, but it’s prompting me to revisit old techniques about which I first learned when I was too stupid (or uncreative) to think of novel ways of going about measuring these topics. **

I’ve been pretty fortunate with my first quarter at UC Irvine, particularly in that the classes I’m taking feed directly into my interests. That might seem like a very low bar for satisfaction, but consider that there are hundreds of classes offered (admittedly maybe only a dozen or so in Anthropology) and I could be taking any of those classes instead. More likely than not there would be some irrelevant classes where I was putting in a significant amount of work for something ultimately not related to my goals. More troubling, though, is that I could be in a class where nothing meaningful got done. ** That would be a waste of units - time and money, in my case - and I’d be pretty upset about that. **

I’m glad I waited a few weeks after coming back from Ecuador to post another update. ** Got home, got myself settled in at Irvine, got a bike, fucked the bike up (don’t ask), and finally got a laptop. **

I was a little drunk when I wrote this, sitting in a Beatles-themed dive bar in Quito while yelling over some Beatles hit to get this point across, so bear with me. Also, immediately afterwards we wandered into a bar where we danced with an English dude who I’m pretty sure was on cocaine. So my memory might be skewed by that whole experience.

These past several days have been difficult. Yesterday UC Irvine notified me that they had not yet received my college transcripts, and were thus putting a hold on my registration. I’m not sure how that impacts my Summer Start registration for classes (which have been processed and confirmed), but HOPEFULLY they’ll keep that registration intact on the premise that I will resolve this issue. Eventually.

After self-prepared dinner last night (french toast and eggs), the group of students staying at la Conventa played drinking games late into the night. I decided to turn in early because I was still feeling sick.

These past four or five days I’ve been getting sicker and sicker, so today I decided to quickly deal with whatever I have rather than fight it on my own over a longer period of time. The doctor experience was odd. There was nothing profoundly different about the place; it just felt off. The doctor’s office itself was more like a business office than a medical office, with his own desk separating himself from patients. His desk was covered in paperwork, upon which was resting a prescription pad, making it seem like his job was more about writing prescriptions and doing paperwork than it was about being with and interacting with his patients.

After a brutally cold shower and a casual, delicious breakfast, the staff organized to discuss last-minute logistics before the students arrived. “The Bed Crew” - consisting of Sofia, Matt, Daniel, and myself - expanded to a group of 8 and raced to finish assembling beds for students participating in the “homestay” program, wherein students stay with locals for the duration of the program.

Today was my second day of lab/babysitting sick people so that Vanessa can hopefully get some work done. Yesterday’s catalog was easy - making a catalog of artifact fragments on which we’ll be doing XRF analysis (someday), but today I was tasked with cleaning the recent finds from the field, mostly ceramics caked in hard dirt.

Yesterday the ladies we employ at the house were given the day off, leaving us to prepare our own breakfast, lunch, and dinner. Breakfast was hectic and a lot of silverware and cups were left out. In hindsight, I realize that the ladies have probably been cleaning up for us this entire time. How they have patience for us is astounding.

Today after cleaning an old dig site we stopped at the mathematically derived equator - the exact middle of the world.

This morning the students and staff went to survey (by transect) the areas between Cangahua and the Hacienda/Lab. My group was assigned to 16 degrees(???) and we headed north from our dropoff point.

The music here pounds through the ground when you walk the streets of Cangahua during a festival. A sea of modestly decorated Andean hats, worn by men and women alike (but adorned by women with a colorful feather, cover the sidewalks and parts of the streets themselves. Others, dressed in ornate and colorful masks, lead a parade which seems to weave throughout the city but focuses on the main street leading up the hill. Behind them follows a truck, outfitted with speakers larger than the musicians and singers who play adjacent to and on the backs of the trucks. Everyone here is a participant. No one, it seems, is just going about their business as usual today. This may be the largest fiesta of the year; it seems like the longest.

I landed in Quito with another student without incident. Our incident was awaiting us, since I had recently received an email from the hostel I booked, telling me they were actually completely full. After a few different flights, we both found ourselves landing in Quito at the same time and we decided to head into town to look for a hostel like any other dumb tourists - by walking door to door.

I tend to find this happening a lot more near the end of the year than anytime else (big surprise), but I’m beginning to feel burnt out from this past year - or more directly, this past year. After wrapping up three full-size studies on campus, tutoring a handful of kids in introductory stats, and getting back rejection letters from my top UC choices, I have to admit that I’ve lost some motivation.

This quarter I’ve been working with a small group of students, trying to get a better handle on the transfer rates at Foothill College within specific majors, GPA brackets, etc…

Yesterday I had a rough day, mostly consisting of me oversleeping - something I rarely do anymore. I don’t sleep much in the first place, so a few hours more is like doubling my sleep. It sounds nice, but it’s disruptive.

I’ve spent quite a bit of time looking at lots of data from various sources (San Francisco, Chicago, and whatever else I can get my hands on). It’s been kind of interesting, but ultimately all I can tell you based on the data is that people sleep at night and cops tend to get called more around lunch. Actually, that last bit is interesting.

In a little under 2 weeks I’m traveling to Ecuador. After Ecuador, I move in to start my first quarter (in the Summer) as a student at UC Irvine, majoring in Anthropology and starting as a Junior.