Socially relevant ethnic groups, ethnic structure, and AMAR
Bibliographic record
Abstract
Abstract Protracted conflicts over the status and demands of ethnic and religious groups have caused more instability and loss of human life than any other type of local, regional, and international conflict since the end of World War II. Yet we still have accumulated little in the way of accepted knowledge about the ethnic landscape of the world. In part this is due to empirical reliance on the limited data in the Minorities at Risk (MAR) project, whose selection biases are well known. In this article we tackle the construction of a list of ‘socially relevant’ ethnic groups meeting newly justified criteria in a dataset we call AMAR (A for All). We find that one of the principal difficulties in constructing the list is determining the appropriate level of aggregation for groups. To address this issue, we enumerate subgroups of the commonly recognized groups meeting our criteria so that scholars can use the subgroup list as one reference in the construction of the list of ethnic groups most appropriate for their study. Our conclusion outlines future work on the data using this expanded dataset on ethnic groups.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".