An Ethnic Polarization Measure with an Application to Ivory Coast Data
Bibliographic record
Abstract
In this paper, we suggest a framework for the analysis of ethnic polarization. This framework allows for the measurement of ethnic or religious polarization. We apply our measure to Ivory Coast and find a surprising result as the ethnic polarization decreased in years preceding the conflict in the country. However, further decomposition of the ethnic polarization index allows us to understand better how the variation in polarization may have induced this conflict. / Dans cet article, nous proposons un cadre d’analyse de la polarisation ethnique. Ce cadre analytique permet d’analyser la polarisation ethnique et religieuse. Nous appliquons ce cadre à des données de la Côte d’Ivoire et constatons que la polarisation ethnique a diminué durant les années précédant le conflit. Par contre, une décomposition plus fine de notre indice de polarisation ethnique nous permet de mieux comprendre comment les variations de polarisation ont pu induire le conflit.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".