Recognition, Redistribution and Redress: The Case of the “Chinese Head Tax”
Bibliographic record
Abstract
Abstract. This article uses the recent Canadian campaign seeking redress for the infamous “Chinese head tax” as a vantage point from which to consider whether recognition-seeking social movements are undermining the cause of egalitarian redistribution. Methodologically, the article seeks to complement the normative theorizing and conceptual model-making that have tended to characterize the “recognition versus redistribution” debate by focusing more concretely on the dynamics of an actual social movement campaign. The article demonstrates how this approach can help to identify important nuances in recognition campaigns that blanket claims about recognition's impact both ignore and serve to obscure. Résumé. Cet article étudie la récente campagne canadienne cherchant la réparation dans les cas d'application de l'infâme taxe d'immigration aux Canadiens d'origine chinoise. Cette campagne offre l'opportunité pour étudier si les mouvements sociaux militant pour la reconnaissance des situations d'abus perpétrées par le passé sont en train d'éroder la cause de la redistribution égalitaire. D'un point de vue méthodologique, l'article essaie de compléter la théoretisation normative et le developpement de modèles conceptuels qui ont seulement pris en compte le débat dit “ de la reconnaissance versus la redistribution ”, en se concentrant plus sur l'étude de la dynamique d'une campagne sociale contemporaine. L'article montre le fait que cette approche peut aider à mettre en exergue d'importants nuances dans les campagnes dites “ de la reconnaissance ”, que des études plus generaux sur l'impact de la reconnaissance ignorent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.020 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".