AI, Big Data, and surveillance zines as forms of community healthcare
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".