The Psychology of Social and Cultural Diversity
Bibliographic record
Abstract
Notes on Contributors. Series Editor s Preface. 1. Introduction (Richard J. Crisp, University of Kent). Part I: Social Identity. 2 : Social identity complexity and acceptance of diversity (Marilynn B. Brewer). 3: Facilitating the development and integration of multiple social identities: The case of immigrants in Quebec (Catherine E. Amiot and Roxane de la Sablonniere). 4: Costs and benefits of switching among multiple social identities (Margaret Shih, Diana T. Sanchez and Geoffrey C. Ho). Part II: Culture. 5: Multicultural identity: What it is and why it matters (Angela-Minh,Tu D. Nguyen and Veronica Benet-Martinez). 6: What I know in my mind and where my heart belongs: Multicultural identity negotiation and its cognitive consequences (Carmit T. Tadmor, Sun No, Ying-yi Hong and Chi-yue Chiu). Part III: Intergroup Attitudes. 7: Multiculturalism and tolerance: An intergroup perspective (Maykel Verkuyten). 8: Diversity experiences and intergroup attitudes (Christopher L. Aberson). Part IV: Intergroup Relations. 9: The effects of crossed-categorizations in intergroup interaction (Norman Miller, Marija Spanovic, and Douglas Stenstrom). 10: Complexity of superordinate self-categories and ingroup projection (Sven Waldzus). Part V: Group Processes. 11: The categorization-elaboration model of work group diversity: Wielding the double-edged sword (Daan van Knippenberg and Wendy P. van Ginkel). 12: Divided we fall, or united we stand? How identity processes affect faultline perceptions and the functioning of diverse teams (Floor A. Rink and Karen A. Jehn). Part VI: Interventions. 13: Combined effects of intergroup contact and multiple categorization: Consequences for intergroup attitudes in diverse social contexts (Katharina Schmid and Miles Hewstone). 14: The application of diversity-based interventions to policy and practice (Lindsey Cameron and Rhiannon N. Turner). Index.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".