Bibliographic record
Abstract
Communal conflicts threaten political stability throughout the world. While conflicts such as those between Hutu and Tutsi or between Bosnian Serbs, Croats, and Muslims degenerated to genocide, others such as those between the Anglophone and Francophone Canadians or between Walloons and Flemings in Belgium have remained remarkably peaceful despite chronic tension. This difference in the level of hostility across cases of identity-based conflict presents a rich field of investigation for political psychology. In addition, increased mobility and intergroup marriage has generated further political challenges to the notion of self-determination. It is increasingly difficult to categorize people and to adapt politically to changing demands, as indicated by debate in several countries over identity categories in censuses. People are increasingly conscious of the fact that they belong to multiple politically salient groups that sometimes have conflicting goals. In light of these difficulties, I present a theoretic basis for studying this interaction. Political studies of communal conflict and psychological research on identity operate at different levels of analysis, but can be combined to produce an explanation of the dynamic nature of identity politics. After a brief review of research in these areas, I present a model to adapt psychological theories of multiple identification to the complex realm of political debate and legitimation. Although there is not space here for a full application of the combination, I use examples from Belgium and Canada to illustrate this perspective. This political psychological perspective can greatly contribute to our understanding of the dynamics of identity-based conflict and efforts to intervene in such conflicts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".