Bibliographic record
Abstract
Les personnes qui preconisent la justice reparatrice pretendent que ces modeles peuvent profiter aux contrevenants et contrevenantes, aux victimes et aux communautes, tout en redressant des injustices historiques perpetrees contre les peuples autochtones. Ces pretentions s'etendent aussi aux cas de violence intime. Dans le cas des cercles de determination de la peine convoques par les tribunaux dans les cas de violence intime au Canada, ces pretentions se sont averees sans fondements. De fait, en comparant les resultats dans ces dossiers aux etudes recentes traitant des besoins des femmes battues, ces modeles, dans leur forme actuelle, n'ont pas reussi a redresser l'injustice et l'inegalite sociales que subissent les femmes dans les communautes autochtones au Canada et, dans certains cas, ont «victimise» a nouveau les survivantes de violence intime. Advocates of restorative justice claim that these models can benefit offenders, victims, and communities and also address historical injustices perpetrated against Aboriginal peoples. These claims extend to cases of intimate violence. In the case of judicially convened sentencing circles in cases of intimate violence in Canada, these claims have not been borne out. In fact, by measuring the outcomes in these cases against recent studies of battered women's needs, these models, as they are currently constituted, have inadequately addressed social injustice and inequality experienced by women within Canadian Aboriginal communities and, in some instances, have revictimized survivors of intimate violence.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.042 | 0.035 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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".