Negative expectancies for the group's outcomes undermine normative collective action: Conflict between Christian and Muslim groups in Lebanon
Bibliographic record
Abstract
In this extension of the social identity model of collective action (SIMCA; Van Zomeren, Postmes, & Spears, 2008), group expectancies are an intervening construct for the impact of group identification, perceived group inefficacy, and perceived group injustice on normative collective action. In addition to the SIMCA path from greater group identification to more action, Hypothesis 1 was that greater identification fosters less negative group expectancies, which, in turn, promote action. Hypothesis 2 was that the SIMCA path from greater perceived group inefficacy to less action is mediated by negative group expectancies. These hypotheses were for low- and high-status groups, as was the expectation for the SIMCA path from greater perceived group injustice to more action. For the low-status group, Hypothesis 3 was that perceived injustice also undermines action by fostering more negative group expectancies. During severe ethno-religious group conflict in Lebanon, university students reported on SIMCA factors and their group expectancies. Results were in line with SIMCA and Hypotheses 2 and 3, and partly with Hypothesis 1. Group expectancies are discussed in relation to likelihood of amelioration, perceived instability, and emotions. Types of expectancies are discussed, as is the relation of expectancies to normative and non-normative collective action.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".