The strategic calculus of terrorism: Substitution and competition in the Israel—Palestine conflict
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous work on the dynamics of conflicts where we see terrorism has tended to focus on whether we see shifts in attack mode following government countermeasures. We contend that many factors other than counterinsurgency can influence whether groups resort to terrorism, including competition between groups, as well as their relationship to public opinion and other political events. Hence, understanding terrorist tactics in prolonged conflicts with multiple actors requires us to consider a more general framework of innovation, imitation, competition and dependence between actors. We use disaggregated data on terrorist attacks, counterterrorism and public opinion in the Israel—Palestine conflict to jointly evaluate predictions derived from several conventional theories of strategic behaviour. We find that the strategic calculus of Palestinian groups is complex and cannot be treated as time invariant. Our results suggest that factors such as the degree of public support, inter-group competition, the anticipation of countermeasures and non-trivial non-violent payoffs have an observable effect on the strategic behaviour of the Palestinian groups, and that structural relationships are often far from constant over time.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it