Bibliographic record
Abstract
Dans cet essai, je m’intéresse aux oeuvres visuelles d’artistes du Canada afin de mieux saisir comment la mémoire noire et les actes de préservation fonctionnent en dehors des contraintes institutionnelles. Ces artistes nous permettent d’imaginer une sensibilité archivistique noire émergeante à partir de leurs choix artistiques et esthétiques concernant la vie des personnes noires. Dans cet essai, je cherche à découvrir comment les artistes insistent sur la vie des personnes noires par le biais de leurs portraits, leurs installations et leurs oeuvres en techniques mixtes. Le projet est ici d’explorer, d’amplifier et de célébrer la façon dont les artistes encouragent leur public à voir la vie des Noir·es de manière ouverte, au-delà des schémas hiérarchiques racialisés et des discours sur la mort. Le « plus de vie » que je signale dans le titre de cet essai fournit un cadre pour que nous puissions découvrir, rassembler et célébrer les articulations de la vie des personnes noires dans un moment de puissance algorithmique implacable.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".