Bibliographic record
Abstract
In what ways does intensified violence change attitudes in protracted conflicts? When does it harden attitudes and when does it moderate them? This question is tested for the post-1967 period in Israeli–Palestinian relations, with emphasis on the two intifadaperiods (1987–93 and 2000 to the present). A clear distinction emerges between ‘expressive’ issues, those with a short-term focus and a high emotive content, and the ‘primary’ issues in the conflict. Violence produced a hardening of positions on expressive issues in both intifadaperiods, as would be predicted in escalation theories. On primary issues, however, violence engendered moderation in the search for an overall solution, in line with ‘ripeness’ theory, when the two major conditions of ‘ripeness’ — perceptions of a mutually hurting stalemate and a ‘way out’ — were met. Thus the first intifadaaccelerated moderating trends on primary issues, while in the second intifada,where perception of a ‘way out’ was much weaker, attitudes on basic issues did not moderate until a basic structural change occurred in late 2004.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".