Bibliographic record
Abstract
In her book ‘Discriminating Data‘ (2021), Wendy Chun reveals how polarization is a goal — not an error — within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Hito Steyerl and Wendy Chun will discuss how can people release themselves from the vice-like grip of discriminatory data and consider alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks. Wendy Hui Kyong Chun is the Canada 150 Research Chair in New Media at Simon Fraser University, and leads the Digital Democracies Institute which was launched in 2019. She studied Systems Design Engineering and English Literature and is author of Control and Freedom (2006), Programmed Visions (2011), and Updating to Remain the Same (2016). Hito Steyerl works as a filmmaker, philosopher, and cultural critic. Her work takes the form of essays, lectures, installations, video, and photography. She is professor for experimental film and video and the co-founder of the Research Center for Proxy Politics at the Berlin University of the Arts.
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 | 0.014 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.093 | 0.044 |
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".