Bibliographic record
Abstract
expanding the peace journalism framework 33 Chapter 3 'Human rights journalism': a critical conceptual framework of a complementary strand of peace journalism 50 Chapter 4 Empathy and ethics: journalistic representation and its consequences 69 PART II CASE STUDIES: PEACE JOURNALISM IN WARTIME AND PEACEBUILDING 87 Chapter 5 Documenting war, visualising peace: towards peace photography 89 Chapter 6 Oligarchy reloaded and pirate media: the state of peace journalism in Guatemala 105 Chapter 7 The gaze of US and Indian media on terror in Mumbai: a comparative analysis 122 Chapter 8 Peace journalism-critical discourse case study: media and the plan for Swedish and Norwegian defence cooperation 141 Chapter 9 Conflict reporting and peace journalism: in search of a new model: lessons from the Nigerian Niger-Delta crisis 158 Chapter 10 Peace process or just peace deal?The media's failure to cover peace 175 v PART III AGENCIES AND OPENINGS FOR CHANGE Chapter 11 Can the centre hold?Prospects for mobilising media activism around public service broadcasting using peace journalism Chapter 12 Globalisation of compassion: women's narratives as models for peace journalism Chapter 13 Examining the 'dark past' and 'hopeful future' in representations of race and Canada's Truth and Reconciliation Commission Notes on contributors Index Contents vi vii firing a bullet is somehow objective whereas saying or doing peace is not.Happy reading -and please join the search for better media!Versonnex, France,
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.205 | 0.057 |
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".