Framing and fighting: The impact of conflict frames on political attitudes
Bibliographic record
Abstract
Abstract How does the subjective conceptual framing of conflict impact the warring parties’ attitudes towards political compromise and negotiation? To assess strategies for conflict resolution, researchers frequently try to determine the defining dispute of a given conflict. However, involved parties often view the conflict through fundamentally distinct lenses. Currently, researchers do not possess a clear theoretical or methodological way to conceptualize the complexity of such competing frames and their effects on conflict resolution. This article addresses this gap. Using the Israeli–Palestinian conflict as a case study, we run a series of focus groups and three surveys among Jewish citizens of Israel, Palestinian citizens of Israel (PCIs), and Palestinians in the West Bank. Results reveal that three conflict frames are prominent – material, nationalist, and religious. However, the parties to the conflict differ in their dominant interpretation of the conflict. Jewish Israelis mostly frame the conflict as nationalist, whereas Palestinians, in both the West Bank and Israel, frame it as religious. Moreover, these frames impact conflict attitudes: a religious frame was associated with significantly less willingness to compromise in potential diplomatic negotiations among both Jewish and Palestinian citizens of Israel. Interestingly, differing frames had no significant impact on the political attitudes of West Bank Palestinians, suggesting that the daily realities of conflict there may be creating more static, militant attitudes among that population. These results challenge the efficacy of material solutions to the conflict and demonstrate the micro-foundations underpinning civilians’ conflict attitudes and their implications for successful conflict resolution.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".