Bibliographic record
Abstract
Abstract This chapter analyzes how racialized feminists have forged specific political vocabularies to name and politicize their relationships with white feminists in the context of the headscarf debates. Their discourses are articulated with a set of emotions and moral dispositions. This chapter captures the formation of (collectively produced) moral, political, and ethical dispositions that are intimately linked to and shaped by the context of postcolonialism and postsecularism in France and Quebec. This chapter argues that by calling themselves feminists, racialized feminists in both contexts enter—among other processes—in relation with white feminists, a relation that they attempt to fashion with their own vocabulary, concepts, and discourses. Racialized feminists seek to create a new language from within a dominant discourse. The chapter explores the political emotions, such as indignation, frustration, pain, unease, anger, or lassitude, that sustain racialized feminists’ relationship to white feminists, and the forms of moral address they convey to white feminists through both resistance and resentment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".