Political psychology : cultural and crosscultural foundations
Bibliographic record
Abstract
List of Tables and Figures Preface PART I: FOUNDATIONS OF CROSSCULTURAL POLITICAL PSYCHOLOGY Cultural and Crosscultural Political Psychology: Revitalizing a Founding Tradition for a New Subfield S.A.Renshon & J.Duckitt The Elusive Concept of Culture and the Vivid Reality of Personality L.Pye The Relevance of Culture for the Study of Political Psychology M.H.Ross Taboo Trade Offs: Constitutive Prerequisites for Political and Social Life A.P.Fiske & P.E.Tetlock Substance and Method in Cultural and Crosscultural Political Psychology S.Renshon, J.Duckitt, M.H.Ross, O.Feldman, F.M.Moghaddam, G.DeVos, W.G.Stephen & K.Leung PART II: CULTURE, PSYCHOLOGY, AND POLITICAL CONFLICT Culture, Personality, and Prejudice J.Duckitt The Political Culture of State Authoritarianism J.Meloen Conflict and Injustice in Intercultural Relations: Insights from the Arab-Israeli and Sino-British Disputes K.Leung & W.G.Stephan Culture and Ethnic Conflict M.H.Ross PART III: THE POLITICAL PSYCHOLOGY CHANGE IN CULTURAL REGIONS The Political Unconscious: Stories and Politics in Two South American Cultures A.Johnson Cultural Nationalism and Beyond: Crosscultural Political Psychology in Japan O.Feldman Change, Continuity, and Culture: The Case of Power Relations in Iran and Japan F.M.Mogaddam & D.Crystal Value Adaptation to the Imposition and Collapse of Communist Regimes in East-Central Europe S.H.Schwartz, A.Bardi & G.Bianchi PART IV: POLITICAL PSYCHOLOGY AND THE DILEMMAS OF MULTICULTURALISM Social Authority and Minority Status: Problems of Internationalization and Alienation Among Japanese and Koreans in Diverse Cultural Settings G.DeVos Multicultural Policy and Social Psychology: The Canadian Experience J.W.Berry & R.Kalin American Identity and the Dilemmas of Cultural Diversity S.A.Renshon 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.008 |
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".