Bibliographic record
Abstract
1949 : H. Garfinkel publie dans Social Forces un article intitulé Research note on inter- and intra-racial homicides, lequel doit « en premier lieu, fournir des matériaux relatifs au traitement réservé aux auteurs blancs et noirs impliqués dans des homicides inter ou intraraciaux, et en second lieu, soumettre une hypothèse qui rende compte des particularités des données mises en évidence lorsque les différents indicateurs de traitement […] sont classés selon la race de l’auteur et de la victime ». Il y montre comment la différenciation raciale du travail pénal reproduit la structuration raciale de la société. Le monde judiciaire selon Garfinkel décrit dans un premier temps les termes et l’articulation de la démonstration à laquelle Garfinkel se livre dans cette étude. Dans un deuxième temps, il explicite le choix de raison où son interprétation de la différenciation raciale du travail pénal s’enracine, avant d’en tirer, dans un troisième temps, les implications pour l’étude du droit, du crime et de la discrimination.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".