Investigating status hierarchies with media analysis: Muslims, Jews, and Catholics in <i>The New York Times</i> and <i>The Guardian</i> headlines, 1985–2014
Bibliographic record
Abstract
Media analyses can help expand our understanding of how hierarchies are expressed and of how they evolve across time and place. In this article, we compare coverage of Muslims, Jews, and Catholics in The New York Times and The Guardian headlines over a 30-year time period. In aggregate, our data show that media portrayals of groups are relatively stable over the span of decades rather than highly sensitive to the impact of events at any given point in time. In keeping with the findings of surveys, Muslims are generally associated with more negativity than Catholics or Jews. At the same time, our data also reveal information that nuances what traditional surveys have shown. For example, Jews are portrayed consistently more positively than Catholics in our analysis; in addition, while headlines about Catholics are more positive than those about Muslims in The New York Times, the tone of headlines about the two groups is indistinguishable in The Guardian. The methods and the findings introduced here contribute to the research agenda of scholars concerned with identifying, tracking, and understanding status hierarchies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".