Unfreezing cognitions during an intractable conflict: Does an external incentive for negotiating peace and (low levels of) collective angst increase information seeking?
Bibliographic record
Abstract
A core feature of intractable conflicts is the tendency to cognitively freeze on existing, pro-ingroup beliefs. In three experiments, conducted in the context of the Palestinian-Israeli conflict, we tested the idea that an external incentive for negotiating peace helps unfreeze cognitions. In Experiment 1, making salient that peace with the Palestinians would reduce the Iranian nuclear threat (an external incentive) led to a process of unfreezing. In Experiment 2, we examined whether collective angst as an emotional sentiment (i.e., concern for the ingroup's future vitality as a temporally stable emotional disposition) moderated the aforementioned external incentive-cognitive unfreezing link. As predicted, external incentive salience promoted cognitive unfreezing, but only among people low in collective angst (i.e., people who are not concerned for the ingroup's future). In Experiment 3, we sought to replicate the results of Experiment 2. However, socio-political forces (i.e., a significant upswing in tensions between Palestinians and Israelis) likely served to freeze cognitions to such an extent that thawing was not possible by the means demonstrated in Experiments 1 and 2. The importance of confidence in a peace process is discussed in the context of efforts to unfreeze cognitions during an intractable conflict.
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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 | 0.007 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".