Psychology of Fear, Crime and the Media
Bibliographic record
Abstract
CONTENTS Acknowledgements ... Introduction ... Section 1 1. Fear of crime as a 'Sponge'. Towards a More Dynamic Understanding of the Relationship Between Generalized Social Attitudes and Fear of Crime ... Stefaan Pleysier and Diederik Cops, 2. Construal Level Theory and Fear of Crime... Ioanna Gouseti and Jonathan Jackson, 3. Madness - Fear and Fascination... Peter Morrall, 4. Media and Fear of Crime: An Integrative Model... Derek Chadee and Mary Chadee, 5. Toward a Social Psychological Understanding of Mass Media and Fear of Crime: More than Random Acts of Senseless Violence... Linda Heath, Alisha Patel and Sana Mulla, 6. Globalization & Media: A Mediator Between Terrorism and Fear A Post 9/11 Perspective ... Sonia Suchday, Amina Benkhoukha and Anthony Section 2 * Fear of Crime from a Multifocal Perspective: From Impersonal Concerns to Crimophobia-based PSDT... Frans Willem Winkel, and Maarten J.J. Kunst, L 8. Cross-cultural examinations of fear of crime: The case of Trinidad and the United States... Jason Young and Danielle Cohen, and Derek Chadee 9. Fear of Gangs: A Summary and Directions for New Research ... Jodi Lane, and James W. Meeker, 10. Mass media, Linguistic Intergroup Bias, and Fear of Crime... Silvia D'Andrea, Michele Roccato, Silvia Russo, and Federica Serafin, 11. Media, Fear of Crime and Punitivity among University Students in Canada and the United States: A Cross-National Comparison... Steven Kohm, Courtney A.Waid-Lindberg, Rhonda R. Dobbs, Michael Weinrath, and Tara O'Connor Shelley, 12. Who's Afraid of the Big Bad Video Game? Media Based Moral Panics... Christopher J. Ferguson, and Kevin M. Beaver, Contributors...
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".