Sara Ahmed. Complaint! Durham, NC: Duke University Press, 2021. 376p. $29.95 (ISBN 978-1-4780-1771-4).
Bibliographic record
Abstract
Living, as we are, in this confluence of catastrophes including climate collapse, the global drug poisoning crisis, and the COVID-19 pandemic, experience tells us that the trouble is not with evidence. The trouble is with power. As we hear more and more testimony and analyze increasing amounts of data about the impacts of racial capitalism, imperialism, patriarchy, and connected ideologies, I find the most urgent writing of our time to be the scholarship of power: how it operates, where it accumulates, and why it persists. In Complaint! Sara Ahmed offers what she calls a “phenomenology of the institution” (19) by interrogating complaint structures and procedures in universities. While Ahmed’s scope is limited to universities, the mechanisms she interrogates and her conclusions are broadly applicable to other institutions. Based on interviews conducted during a 20-month period “with forty students, academics, researchers, and administrators who had been involved in some way in a formal complaint process, including those who did not take their complaints forward, who started the process only to withdraw from it” (10), Ahmed presents a careful and sophisticated analysis of power and its abuses in universities.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.458 | 0.409 |
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".