Bibliographic record
Abstract
Introduction: The symposium target paper in broader context Part 1: Does Ethnic Heterogeneity Depress Public Altruism in Multi-Ethnic Societies? 1. Urban Begging and Ethnic Nepotism in Russia: An ethnological pilot study 2. Ethnic Diversity, Population Size and Charitable Giving at the Local Level in the United States 3. Ethnic Heterogeneity and Public Spending: Testing the evolutionary theory of ethnicity with cross-national data 4. An Exploratory Comparative Study of the Relationship between Ethnic Heterogeneity and Welfare Politics 5. Reconciling the Differences between Sanderson's and Vanhanen's Results Part 2: Welfare Broadly Defined 6. Ethnic Heterogeneity and Economic Growth: Ethnolinguistic diversity, government and growth 7. Ethnic Diversity, Foreign Aid, Economic Growth, Population Policy, Welfare, Inequality, Conflict and the Costs of Globalism: A perspective on W. Masters and M. McMillan's findings Part 3: Explanation and Prediction: Does evolutionary theory help? 8. The Limits of Chimpanzee Charity: Strategies of meat sharing in communities of wild apes 9. Selfish Cooperation, Loyalty Structures and Proto-Ethnocentrism in Intergroup Agonistic Behaviour 10. Canadian Welfare Policy and Ethnopolitics: Toward an evolutionary model 11. Why Welfare States Rise and Fall: Ethnicity, belief systems, and environmental influences on the support for public goods Part 4: Ethical and Policy Implications 12. Ethnicity, the Problem of Differential Altruism, and International Multiculturalism 13. Affirmative Action and Ethnic Nepotism 14. The Evolutionary Deficit in Mainstream Political Theory
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".