[🪐 Bootcamp Week 5] 🚀 Reading to gas you up as you work with community organizations 🚀

[jane2620]

Do

• join the Zulip stream for the topic you will be working on for the rest of the summer: #work & labor, #criminal legal system, #immigration, #health & healthcare, #education
• post an introductory message on the stream and describe:
  • what your personal reach and stretch goals are in understanding this topic
  • thoughts on how feasible the community organizations' goals are in the below documents
  • links to any datasets you are curious to analyze and might need help on
  • potential people you might email to ask for a 15-30 minute chat to help you understand nuances around the research questions

Ask Jaan and/or your team leads if you are having trouble searching for people like this. This skill of identifying stakeholders with whom to conduct informational interviews is crucial for data thinking, and understanding edges of maps, nuances of data, and incentives behind its collection, curation, and the incentives of the community organizations. Consider asymmetric information you might be privy to in these interviews, and organizations that have the most mindshare in the area you are working in, and consider reaching out to them first.

• Practice using the toolkit and skills you have developed throughout the bootcamp to create a visualization for one of the community organizations we did in the breakout session -- run this notebook and create an alternative visualization of the data: https://github.com/onefact/datathinking.org-codespace/blob/main/notebooks/princeton-university/week-5-logistic-regression-community-partner-data.ipynb (visualizations here
• Draft a 3-sentence email to someone you identified could be worth chatting with using the following template: context, question, schedule.

Context sentence: describe who you are, what you are doing, and your motivation for helping the community organization with their reserach question. Question: a specific yes/no question that is easy to decline for the recipient [notice how this implicitly uses the principles of "once upon" in the readings of providing options to enhance agency]. Schedule: describe your specific availability in the next week or two. Post these three sentences in the Zulip stream with your team and decide if it is worth sending, or ask Jaan for feedback directly.

• Write a short journal entry somewhere private about what percentile you believe you are at given your awareness of what degree the community organization is or is not aware of best practices surrounding data, data analysis, whether you believe you can help them, what support you believe you might need in achieving your goals for the summer.

Feel free to append to this journal entry throughout the summer to reflect on it. More often than not, you might find that you are the expert in the room and called to rely on your instinct, judgment, knowledge, and skills in asking for help and leaning on a variety of resources in helping yourself, your team, and your community organization make sense of complex data surrounded by a plethora of incentives no one understands in much detail. Paying attention to your experience navigating your confidence as you practice asking for help and feeling entitled in doing so can help.

• Pick a service, place, thing, entity you interact with regularly, and find the paths through which it may or may not be amenable to input/feedback. If you can't find this, find the legal person responsible (property: manager or parent management company; company: executive-level person; service: help line/number/email). Prioritize your issues with this service. Then ask GPT to help you understand the incentives of the system you are interacting with, and to help phrase your feedback into a few short sentences hinting that you understand these incentives, and ideally ending with a yes/no question. Submit this feedback, paying attention to your experience and perceived locus of control (does doing this and advocated for yourself and other users feel useful or futile to notice sharp or blurry edges of the map? Legible or illegible systems? Do you believe this 'data' is an accurate reflection of your experience, or does the submission mechanism make it less so?).

Read

• Read the goals of the community organizations and the descriptions of the datasets they are making available to you: https://docs.google.com/document/d/1eRARhom1yk1UdPpPyfOAKm9rMDXqlG43fmS0QFMwzOE/edit?usp=sharing
• Read this zine and perhaps print it out if it seems helpful! https://jvns.ca/wizard-zine.pdf
• Read about active listening and motivational interviewing through a storytelling lens: https://docs.google.com/document/d/194ofWUo9gAVxM7GlvCNDp8GP1a2xHt3TyW58J48H4b4/edit?usp=sharing

Consider how ethnographic research, surveys, qualitative data relies on interpersonal skills like this, and how much data and data analysis depend on things like trust, rapport, empathy among different stakeholders. Might you practice some of these skills in an informational interview related to your work with the community organization your team is working with?

• Read about mirror neurons and how some ethnographers think about collecting data based on their experiences: https://drive.google.com/file/d/1JYDrFMUaH3sD80ElfLuwXiv8X3on8tMB/view?usp=sharing
• Read about the stratifying power of emotion: https://drive.google.com/file/d/1gIMYoXXPDLybhqYjLCDffxX7dLTatKZX/view?usp=sharing
• Read about cross-cultural comparisons may have influenced global geopolitics: https://www.palladiummag.com/2021/10/11/the-triumph-and-terror-of-wang-huning/
• Read about COVID: https://archive.ph/L2BSO (https://www.thetimes.co.uk/article/inside-wuhan-lab-covid-pandemic-china-america-qhjwwwvm0) and skim https://www.uptodate.com/contents/covid-19-evaluation-and-management-of-adults-with-persistent-symptoms-following-acute-illness-long-covid

Get a sense of what doctors care about when defining a research question or disease for an active area such as studying the long term sequelae of coronavirus disease. Where are the edges of the maps? What do we know about them, and might there even be any data to be found?

• Read about how Pixar tells stories: http://web.archive.org/web/20170208134519/http://storyshots.tumblr.com/post/25032057278/22-storybasics-ive-picked-up-in-my-time-at-pixar
• Read about how a writer writes about writing: https://georgesaunders.substack.com/p/on-worry

Consider how you can use the same tools we have been learning (GPT, visualization, data analysis) to use storytelling, narrative devices, data to your advantage in telling stories that matter to you beyond this summer.

• Read these 10 pages on storyboarding a research question and ways to answer it with post-its on a wall, and think about whether it might be worth trying it out over the upcoming weeks: https://drive.google.com/file/d/13TROfdnueRFFYptMUElhWclxO4EluFGL/view?usp=sharing
• Read about the principles of interaction at interactionprinciples.org.

Which do you agree with? Disagree with? Are these too reductive or too broad?

• Read about methods ethnographers and anthropologists use in fieldwork to collect "data" about culture through beliefs, practices, rituals implicit in observed patterns of behavior and visual data: https://drive.google.com/file/d/1F9zr1pHesGPSOTtXLQu4hTxOGu7zoIRx/view?usp=sharing
• Supplemental -- if you are curious to learn more, read the excerpts from Black Heart, Laughing Saints, and When Your Data Make You Cry: https://drive.google.com/file/d/1gIMYoXXPDLybhqYjLCDffxX7dLTatKZX/view?usp=drive_link

Watch

• Pick an episode of "Can't Get You Out of My Head" (https://thoughtmaybe.com/cant-get-you-out-of-my-head/)

Reflect on the above readings about what data can and can't capture, how emotions can be considered data we all receive and perceive, and how many decisions that may seem to be influenced by data might just be the result of a small number of people making decisions based on emotions

• Watch the guest data thinking lecture on visualization and style guides that are worth using with your community organization and the altair visualization library we have been using -- such as having a title for every visualization that tells a story, and a subtitle that gives more detail: https://panopto.ut.ee/Panopto/Pages/Viewer.aspx?id=1b8a6e6c-3188-44ab-a9cb-afcd00592b44

[jaanli]

Thank you for being so on top of this @jane2620 ! Just added this -- feel free to consider it optional as you already are well on the way here :)

• Finally, read about the Panama Papers and consider "how global elites make their money and the ways that they make meaning out of the moral dilemmas they face by having to play in the gray.": https://drive.google.com/open?id=1SIm9mUP5uzlwFYQUMe-EITKMjMY4l9FR&usp=drive_fs

In the first several weeks of the data thinking bootcamp, you learned to use software (a Jupyter notebook, linked here) to analyze ten times as many rows of data as there are Panama Papers (~11.5 million versus 33 million). Consider your goals in data thinking, the Just Data Lab, and career writ large. What aspects of context, access, status games (alluded to in the appendix here), roles of gender, race, ethics, morality, economics, sociology and anthropology might affect the questions you ask, who you get to work with? What support do you need to align these often disparate prerequisites to reach your goals and practice data thinking at the scale of patterns in tens or hundreds of millions of datapoints as you have the tools and skills to do now? Feel free to share in Zulip and we will do our best to help.