Peace Journalism, War and Conflict Resolution
Bibliographic record
Abstract
Contents: John Pilger: Foreword - Richard Lance Keeble/John Tulloch/Florian Zollmann: Introduction: Why peace journalism matters - Clifford G. Christians: Non-violence in philosophical and media ethics - Oliver Boyd-Barrett: Recovering agency for the propaganda model: The implications for reporting war and peace - Richard Lance Keeble: Peace journalism as political practice: A new, radical look at the theory - Jake Lynch: Propaganda, war, peace and the media - Annabel McGoldrick/Jake Lynch: A global standard for reporting conflict and peace - Agneta Soderberg Jacobson: When peace journalism and feminist theory join forces: A Swedish case study - Valerie Alia: Crossing borders: The global influence of Indigenous media - Florian Zollmann: Iraq and Dahr Jamail: War reporting from a peace perspective - Pratap Rughani: Are you a vulture? Reflecting on the ethics and aesthetics of atrocity coverage and its aftermath - Donald Matheson/Stuart Allan: Social networks and the reporting of conflict - Jean Lee C. Patindol: Building a peace journalists' network from the ground: The Philippine experience - Milan Rai: Peace journalism in practice - Peace News: For non-violent revolution - Sarah Maltby: Mediating peace? Military radio in the Balkans and Afghanistan - Susan Dente Ross/Sevda Alankus: Conflict gives us identity: Media and the 'Cyprus problem' - Marlis Prinzing: The Peace Counts project: A promoter of real change or mere idealism? - John Tulloch: Conscience and the press: Newspaper treatment of pacifists and conscientious objectors 1939-40 - James Winter: War as peace: The Canadian media in Afghanistan - David Edwards: Normalising the unthinkable: The media's role in mass killing - Stephan Russ-Mohl: US coverage of conflict and the media attention cycle - Rukhsana Aslam: Perspectives on conflict resolution and journalistic training - Jeffery Klaehn: Afterword.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".