La couverture du crime par la presse : un portait fidèle ou déformé ?
Bibliographic record
Abstract
The authors examined, through content analysis, some criticisms that have been levelled at the press in its coverage of crime. The propositions examined included the accusations that newspapers are preoccupied with violence and “street” crime, that they focus on the bizarre, are superficial in their reporting of crime, misinform the public about the characteristics of offenders and victims, and exhibit a conservative bias in their analysis. This study of a major Canadian daily newspaper revealed substantiation for some of these claims but failed to support others. Violent and street crimes received disproportionate coverage and very few articles contained an in-depth analysis of the roots of crime or the workings of the criminal justice system. The evidence was less clear or non-existent in relation to the claims that the press focus on nonroutine events, that they provide distorted images of offenders and victims and that they have a conservative bent. Commentant l'impact des masse-médias, Marshall McLuhan (1978) soulignait: To invade the private person, or to invade a group with teaching, with doctrines, with entertainment, all these are alike forms of violence. To assume the right to program the sensibilities or thoughts and fantasies of individuals or groups, has long been taken for granted as a viable form of personal or social action... Today, however, there is a new dimension in all of these activities. Electric media move information and people at the speed of light. It is this instant and total quality that constitutes the condition of mass man and the mass society (p. 212).
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".