Bibliographic record
Abstract
Patterns of migration and the forces of globalization have brought the issues of mixed race to the public in far more visible, far more dramatic ways than ever before. Global Mixed Race examines the contemporary experiences of people of mixed descent in nations around the world, moving beyond US borders to explore the dynamics of racial mixing and multiple descent in Zambia, Trinidad and Tobago, Mexico, Brazil, Kazakhstan, Germany, the United Kingdom, Canada, Okinawa, Australia, and New Zealand. In particular, the volume's editors ask: how have new global flows of ideas, goods, and people affected the lives and social placements of people of mixed descent? Thirteen original chapters address the ways mixed-race individuals defy, bolster, speak, and live racial categorization, paying attention to the ways that these experiences help us think through how we see and engage with social differences. The contributors also highlight how mixed-race people can sometimes be used as emblems of multiculturalism, and how these identities are commodified within global capitalism while still considered by some as not pure or inauthentic. A strikingly original study, Global Mixed Race carefully and comprehensively considers the many different meanings of racial mixedness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".