How Discrimination Narratives Resolve Ambiguity: The Case of Islamophobia in Quebec
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Discrimination frequently appears in ambiguous rather than overt forms. How do individuals manage the challenges associated with ambiguous discrimination, such as classifying incidents of negative but ambiguous treatment? Building on studies of microaggressions and perceived discrimination, this article develops an explanation rooted in a novel theory of discrimination narratives. Discrimination narratives express collective beliefs about discrimination’s patterns and features, which enable individuals to resolve ambiguity in their personal experiences and expectations. Based on a study of perceived Islamophobia in the Canadian province of Quebec, the article describes one common discrimination narrative and uncovers how Muslim Quebecers use it to 1) classify negative but ambiguous treatment by imputing missing information; (2) direct their attention to social situations they perceive to be high-risk; and (3) adjust to anticipated patterns in discrimination. Implications for research on ambiguity, microaggressions, perceived discrimination, and narratives are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it