The Rwandan genocide and the media: a two-stage analysis of newspaper coverage
Bibliographic record
Abstract
The Rwandan genocide exhibited a faster rate of killing than any genocide in recent history, taking place over 100 days; however, at the time of its occurrence, it was relatively ignored by the international community. In 2005, Major General Romeo Dallaire singled out the Western press coverage and condemned it for its failure to adequately publicize the genocide. Nevertheless, few studies have analysed the media’s coverage of the genocide and no studies have looked at Canadian media or the criminal aspects of the genocide reporting. This study examined articles printed in the New York Times and the Globe and Mail and consisted of a two-stage content and discourse analysis. The content analysis involved analysis of 17 variables in 577 articles, while the discourse analysis examined the extent to which common themes associated with crime served as a framework for making sense of the Rwandan genocide in 311 articles. As part of the discourse analysis, the data was assessed through a cultural criminological perspective which focused on five criminological themes; crime, perpetrators, victims, law enforcers and law and order. Overall, the results show that Rwanda was presented in the media as a chaotic and primitive country, in many ways beyond the reach of law, and therefore the language of crime was rarely used to describe the genocide. The planning, organization and systematic perpetration of the genocide were largely ignored and the media instead presented genocide in Rwanda as a natural and anarchic result of a primitive and tribal society.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".