Bibliographic record
Abstract
Prelude 1 Amadasun, "Black People"; Charland, "African American Youth"; Tyson, Darrity, and Castellino, "It's Not 'a Black Thing.'" 2 Inzlicht and Schmader, Stereotype Threat; Steele, Whistling Vivaldi. 3 Brand, Bread, 172. 4 Shahjahan, "Being 'Lazy'"; L.T. Smith, Decolonizing Methodologies. 5 Bakan and Dua, "Introducing the Questions," 8. 6 Fanon, Black Skin.7 Martell, "Slow University," para.16. 8 Mountz et al., "For Slow Scholarship," 8. 9 See also Sara Ahmed, Willful Subjects, 50-3, on the additional work of "being in time" with the institution that is required by some of us in order to appear cordially willing rather than wilful, difficult, and in the way.10 See "Occupons McGill!A Letter from the Fifth Floor Occupiers," http:// rabble.ca/news/2011/11/occupons-mcgill-letter-fifth-floor-occupiers.11 For more on the events of 10 November 2011 at McGill, see Sharp and Roberts, "Crisis at McGill"
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.344 | 0.166 |
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".