Explaining the breakdown of ethnic relations : why neighbors kill
Bibliographic record
Abstract
Notes on Contributors. Series Preface. Acknowledgments. 1 Why Neighbors Kill: An Overview: Richard A. Vernon and Victoria M. Esses (University of Western Ontario). Part I. Individual Factors. 2 Extreme Harmdoing: A View from the Social Psychology of Justice: Carolyn L. Hafer (Brock University), James M. Olson (University of Western Ontario), and Alexandra A. Peterson (University of Toronto). 3 On the Nature of Contemporary Prejudice: From Subtle Bias to Severe Consequences: John F. Dovidio and Adam R. Pearson (Yale University), Samuel L. Gaertner (University of Delaware), and Gordon Hodson (Brock University). 4 Why Neighbors Kill: Prior Intergroup Contact and Killing of Ethnic Outgroup Neighbors: Miles Hewstone (University of Oxford), Nicole Tausch (Cardiff University), Alberto Voci (University of Padova), Jared Kenworthy (University of Texas at Arlington), Joanne Hughes (Queen's University Belfast), and Ed Cairns (University of Ulster). 5 Why Neighbors Don't Stop the Killing: The Role of Group-Based Schadenfreude: Russell Spears (Cardiff University/University of Amsterdam) and Colin Wayne Leach (University of Sussex). Part II. Societal Factors. 6 When Neighbors Blame Neighbors: Scapegoating and the Breakdown of Ethnic Relations: Peter Glick (Lawrence University). 7 The Influence of the Threatening Transitional Context on Israeli Jews' Reactions to Al Aqsa Intifada: Daniel Bar-Tal and Keren Sharvit (Tel-Aviv University). 8 Why Do States Kill Citizens? Or, Why Racism is an Insufficient Explanation: Patricia Marchak (University of British Columbia). Part III. Synthesis. 9 Theories of Genocide: The Case of Rwanda: Howard Adelman (Griffith University). 10 Applying the Unified Instrumental Model of Group Conflict to Understanding Ethnic Conflict and Violence: The Case of Sudan: Victoria M. Esses (University of Western Ontario) and Lynne M. Jackson (King's University College at the University of Western Ontario). 11 The Origins of Genocide and Mass Killing, Prevention, Reconciliation, and their Application to Rwanda: Ervin Staub (University of Massachusetts at Amherst). Index
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.018 |
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".