Bibliographic record
Abstract
Abstract For decades, scholars and observers have criticized negative media portrayals of Muslims and Islam. Yet most of these critiques are limited by their focus on one specific location, a limited time period, or a single outlet. This book offers the first systematic, large-scale analysis of American newspaper coverage of Muslims through comparisons across groups, time, countries, and topics. It demonstrates conclusively that coverage of Muslims is strikingly negative by every comparative measure examined. Muslim articles are negative relative to those touching on Catholics, Jews, or Hindus, and to those mentioning marginalized groups within the United States as diverse as African Americans, Latinos, Mormons, and atheists. Coverage of Muslims has also been consistently and enduringly negative across the two-decade period from 1996 through 2016. This pattern is not unique to the United States; it also holds in countries such as Britain, Canada, and Australia, although less so in the Global South. Moreover, the strong negativity in the articles is not simply a function of stories about foreign conflict zones or radical Islamist violence, even though it is true that terrorism and extremism have become more prominent themes since 9/11. Strikingly, even articles about mundane topics tend to be negative. The findings suggest that American newspapers may, however inadvertently, contribute to reinforcing boundaries that generate Islamophobic attitudes. To overcome these drawbacks, journalists and citizens can consciously “tone-check” the media to limit the stigmatizing effect of negative coverage so commonly associated with Muslims and Islam.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.027 |
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".