The Paradox of the Moderate Muslim Discourse: Subtyping Promotes Support for Anti-muslim Policies
Bibliographic record
Abstract
Tolerant discourse in the United States has responded to heightened stereotyping of Muslims as violent by countering that “not all Muslims are terrorists.” This subtyping of Muslims—as some radical terrorists among mostly peaceful “moderates”—is meant to protect a positive image of the group but leaves the original negative stereotype unchanged. We predicted that such discourse may paradoxically increase people’s support of anti-Muslim policies because the subtyping and its associated negative stereotypes justify hostile actions toward Muslims. In Study 1, subtyping predicted support for three anti-Muslim policies, but only among political moderates and conservatives. In Study 2, participants who were exposed to subtyping narratives expressed greater support for surveillance of Muslims in the United States. The effect of subtyping narrative exposure was stronger on support for hawkish anti-terror policy when participants’ preexisting endorsement of subtyping was low. Irrespective of the well-meaning intentions of peaceful vs. radical subtyping, its expression can justify ongoing “War on Terror” policies. As the population of Muslims increases in North America, the intuition that most Muslims do not meet the negative stereotype may ironically reduce inclusion.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".