Data Segregation and Algorithmic Amplification: A Conversation with Wendy Hui Kyong Chun
Bibliographic record
Abstract
Wendy Hui Kyong Chun is Simon Fraser University’s Canada 150 Research Chair in New Media in the School of Communication. She has studied both systems design engineering and English literature, which she combines and mutates in her current work on digital media. She is author of Control and Freedom: Power and Paranoia in the Age of Fiber Optics (Chun, 2006), Programmed Visions: Software and Memory (MIT, 2011), Updating to Remain the Same: Habitual New Media (Chun, 2016), and co-author of Pattern Discrimination (Apprich, Chun, Cramer, & Steyerl, 2018). She has been Professor and Chair of the Department of Modern Culture and Media at Brown University, where she worked for almost two decades and where she is currently a Visiting Professor. She has also been a Visiting Scholar at the Annenberg School at the University of Pennsylvania, a member of the Institute for Advanced Study (Princeton), and she has held fellowships from the Guggenheim, ACLS, the American Academy of Berlin, and the Radcliffe Institute for Advanced Study at Harvard. She has been a Visiting Professor at AI Now at New York University, the Velux Visiting Professor of Management, Politics and Philosophy at the Copenhagen Business School, the Wayne Morse Chair for Law and Politics at the University of Oregon, Visiting Professor at Leuphana University (Luneburg, Germany), and a Visiting Associate Professor in the History of Science Department at Harvard, where she is an Associate.
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 | 0.027 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".