Bibliographic record
Abstract
Abstract Crime stories attract audiences and social buzz, but they also serve as prisms for perceived threats. As immigration, technological change, and globalization reshape our world, anxiety spreads. Because journalism plays a role in how the public adjusts to moral and material upheaval, this unease raises the ethical stakes. Reporters can spread panic or encourage reconciliation by how they tell these stories. Murder in Our Midst uses crime coverage in select North American and Western European countries as a key to examine culturally constructed concepts like privacy, public, public right to know, and justice. Working from close readings of news coverage, codes of ethics and style guides, and personal interviews with almost 200 news professionals, this book offers fertile material for a provocative conversation. The findings divide the ten countries studied into three media models. The book explores what the differing coverage decisions suggest about underlying attitudes to criminals and crime and how justice in a democracy is best served. Today, journalists’ work can be disseminated around the world without any consideration of whether what’s being told (or how) might dissolve cultural differences or undermine each community’s right to set its own standards to best reflect its citizens’ values. At present, unique reporting practices persist among the three models, but the Internet and social media threaten to dissolve distinctions and the cultural values they reflect. There is a need for a journalism that both opens local conversations and bridges differences among nations. This book is a first step in that direction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".