Shaping the Northern Media's Human Rights Coverage, 1986—2000
Bibliographic record
Abstract
Abstract What influences the Northern media's coverage of events and abuses in explicit human rights terms? Do international NGOs have an impact, and, if so, when are they most effective? This article addresses these questions with regression analysis of human rights reporting by The Economist and Newsweek from 1986 to 2000, covering 145 countries. First, it finds that these two media sources cover abuses in human rights terms more frequently when they occur in countries with higher levels of state repression, economic development, population, and Amnesty International attention. There is also some evidence that political openness, number of battle-deaths, and civil societies affect coverage, although these effects were not robust. Second, it finds that Amnesty International's press releases appear to have less impact on media coverage when discussing abuses in countries that are central to the media's zone of concern. Indeed, Amnesty's press advocacy may be more effective when addressing violations in lesser-noticed countries. The article attributes this to the saturation of coverage of abuses in highly mediatized countries. Cumulative attention by multiple journalists and others raises a country's media profile but also makes it more difficult for any one voice to be heard. The authors conclude that Amnesty's press advocacy may have greater media impact when focusing on abuses in countries located away from the media's core areas of concern. Overall, the authors are encouraged by the Northern media's sensitivity to actual patterns of repression and to Amnesty's lobbying, since both indicate that the media is potentially a useful ally in efforts to combat abuses worldwide. Yet, the discouraging effects of poverty on the media's human rights coverage are cause for concern.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".