Media Coverage of Muslim Devotion: A Four-Country Analysis of Newspaper Articles, 1996–2016
Bibliographic record
Abstract
Scholars have identified Muslims’ religiosity and faith practices, often believed to be more intense than those of other religious groups, as a point of friction in liberal democracies. We use computer-assisted methods of lexical sentiment analysis and collocation analysis to assess more than 800,000 articles between 1996 and 2016 in a range of British, American, Canadian, and Australian newspapers. We couple this approach with human coding of 100 randomly selected articles to investigate the tone of devotion-related themes when linked to Islam and Muslims. We show that articles touching on devotion are not as negative as articles about other aspects of Islam—and indeed that they are not negative at all, on average, when focused on a key subset of devotion-related articles. We thus offer a new perspective on the perception of Islamic religiosity in Western societies. Our findings also suggest that if newspapers strive to provide a more balanced portrayal of Muslims and Islam within their pages, they may seek opportunities to include more frequent mentions of Muslim devotion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".