AI, Big Data, and surveillance zines as forms of community healthcare
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT This article analyzes the zines, handbooks, and pamphlets on AI, Big Data, and surveillance published in the United States between 2009 and 2020 that aim to democratize knowledge on technologies. The main texts chosen for this article are A People's Guide To AI: A beginner's guide to understanding AI (2018), Digital Defense Playbook/Cuaderno De Juegos De Defensa Digital (2018), Oh! The Places Your Data Will Go (2019), The People's Field Guide to Spotting Surveillance Infrastructure (2019) and the Coveillance Toolkits (2021), the Stop LAPD Spying Coalition's zines (2020); and the five zines produced by the Detroit Digital Justice Coalition since 2009. These publications are part of a longer history of feminist activists printing zines, booklets, and pamphlets to make scientific knowledge more accessible. In particular, these publications build on the traditional use of zines and handbooks by feminist and health advocacy organizations such as the Boston Women's Health Collective and ACT UP in the United States. In addition to following in their suit of explaining technical information by using clear language and providing definitions and resources, these publications on AI, Big Data, and Surveillance are themselves a form of health literacy.KEYWORDS: AIBig Datazinesfeminismsurveillance AcknowledgementsThank you to our peer reviewers for your feedback and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. While zines created after widespread internet adoption still work to provide access to information, this is not their only benefit. Zines that spread via the internet also allow for activists and organizers to learn about topics and clarify how to talk about them with community members and offer avenues for new metaphors and framings of the topics at hand to resonate with community members. Thank you to our reviewers for pointing out this distinction.Additional informationFundingThe work was supported by the Social Sciences and Humanities Research Council of Canada [Insight Grant 253028].Notes on contributorsAlex KetchumAlex Ketchum has been the Faculty Lecturer of the Institute for Gender, Sexuality, and Feminist Studies of McGill University since 2018. She is the Director of the Just Feminist Tech and Scholarship Lab and the organizer of the SSHRC (Social Science and Humanities Research Council of Canada) funded, Disrupting Disruptions: the Feminist and Accessible Publishing and Communications Technologies Speaker and Workshop Series. This research is supported by her multi-year SSHRC Insight Grant.Nina MorenaNina Morena is a PhD Candidate in Communication Studies at McGill University. Her research investigates the social media practices of young people with metastatic breast cancer. She holds an MA in Media Studies from Concordia University and a BA in English Literature from McGill University.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it