Bibliographic record
Abstract
Abstract.Between 1885 and 1923 Canada imposed a discriminatory head-tax on Chinese immigrants. In 2006 Canada implemented a material redress program intended to resolve this historical injustice, but aspects of this program have been subjected to vigorous criticism by those seeking greater inclusivity. Paying particular attention to the program's intergenerational aspects, this study explores how the current program's conceptualization of a valid redress claim is situated with respect to both its critics and to domestic and international precedents. Recognizing the dynamic potentiality of redress, the study explores aspects of why and how Canada's understandings of historical redress are politically implicated. Résumé.Entre 1885 et 1923 le Canada a imposé un impôt discriminatoire aux immigrés chinois. En 2006, le Canada a mis en oeuvre un programme de réparation afin de redresser cette injustice historique, mais certains aspects de ce programme sont vivement critiqués par les partisans d'une plus grande inclusivité. Se concentrant en particulier sur les aspects intergénérationnels du programme, cette étude analyse la manière dont le programme actuel situe la conceptualisation d'une demande légitime de réparation en fonction à la fois de ses détracteurs et de précédents nationaux et internationaux. Prenant en compte le potentiel dynamique de la réparation, cette étude analyse les implications politiques de la démarche canadienne de redressement historique.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".