Global multiculturalism : comparative perspectives on ethnicity, race, and nation
Bibliographic record
Abstract
Chapter 1 Introduction: National Boundaries/Transnational Identities Part 2 Part I: Multi Culturalism and Ethnicity: Contested Ownership of National Culture Chapter 3 Miscegenation as a Metaphor for Nation Building: The 'Douglarisation' Controversy in Trinidad and Tobago Chapter 4 The Chinese in Thailand: Ethnicity and Power Chapter 5 To Be French: Franco-Maghrebians and the Commission de la Nationalite Chapter 6 Spectacular Imaginings: Performing Community in Guatemala Part 7 Part II: Multiculturalism and Race: Alternative Constructions of Black and White Chapter 8 Brazil: Interactions and Conflicts in a Multicultural Society Chapter 9 Songs in a Strange Land: Race, Dual Consciousness, and the Narrative of African-American Identity in the United States Chapter 10 Letting the Side Down: Personal Reflections on Colonial and Independent Kenya Chapter 11 Race in the Formation of Cuban National and Cultural Identity Part 12 Part III: Multiculturalism and Politics: Constitutional Approaches to Inequality Chapter 13 The Zimbabwe Constitution: Race, Land Reform and Social Justice Chapter 14 Bosnia: Two Days in November Chapter 15 The Crisis of the Mexican State and the Nation: Chiapas as Metaphor Chapter 16 China's Ethnicities: State Ideology and Policy in Historical Perspective Chapter 17 Official Multiculturalism in Canada: Between Virtue and Politics
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".