Testing the social identity‐intergroup differentiation hypothesis: ‘We're not American eh!’
Bibliographic record
Abstract
The social identity-intergroup differentiation hypothesis is a hotly debated issue among social identity researchers (Brown, 2000; Turner, 1999); it states that individuals having a stronger in-group identification will perceive greater differences between their in-group and a relevant out-group. This study examines the importance of three factors when testing this hypothesis: the strength and salience of in-group identification, the relevance of the out-group for social comparison, and the relevance of the dimension of social comparison. The hypothesis was examined in relation to the national identity of a sample of Canadian students. Perceptions of the in-group and out-groups were measured at Time 1 (N =171). The same measures were given at Time 2 (N = 77), along with a variety of measures of social identity. It was predicted that this hypothesis would be supported when the dimension of social comparison was of high relevance and only for an important social comparison group (i.e. Americans). Finally, the ability of identity to predict differentiation at another point in time was examined in order to examine the issue of identity salience and stability. Results generally supported the hypotheses and are discussed in relation to prior research and the conceptualization of a minority identity.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".