Framing Islam in News Reporting: A Comparative Content Analysis
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
The emergence of modern communication technology suggests that the society as a whole is now a simple hostage at the hands of the media. However, the time has come to ask whether the people are being managed, manipulated, massaged or brainwashed by the media. Media contents are unjustifiably dominated by expressions that create negative impressions of Islam. As a result, the media accentuate anti-Muslim bias and bigotry. This study aims to comparatively examine how Nigerian and Malaysian newspapers frame Islam-related events in news reporting. Using purposive sampling, Punch and Vanguard were chosen from Nigeria while The Star and New Straits Times were chosen from Malaysia based on their popularity and readership. Relevant news articles that focus upon reports about Islam or Muslims were collected from the selected newspapers using internet-based search from November 2015 until September 2016. The newspapers produced 599 different Islam-related news articles within this period. The study found that out of 599 news articles published in the selected newspapers, 228 portrayed Islam in conflict situation by using conflict frame. For the rest, 60 news articles used consequence frame, 32 used crime frame, 11 used responsibility frame, 19 used morality frame, and 249 portrayed Islam using human interest frame. Nigerian newspapers used more conflict frames in reporting Islam than Malaysian newspapers. Collective efforts of journalists, editors, and corporate ownership of the newspapers should be directed toward suppressing the negative media portrayal of Islam.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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