Democratic Norms Remain Stronger than Ethnic Ties: Defending “Foreign Interventions and Secessionist Movements”
Bibliographic record
Abstract
Abstract.This article is a response to Stephen Saideman's criticism of our research findings on third state intervention in secessionist crises, which was published in this journal in 2005. Here we defend our methodology and the validity of our results. We also explain why, in our view, Saideman's criticisms and the alternative research design that he offers are seriously questionable. More specifically, our reply focuses on his problematic case selection and on his measurement of ethnic ties, which is methodologically inconsistent and biased. Résumé.Cet article constitue une réponse à la critique de Stephen Saideman concernant nos résultats de recherche, paru dans cette revue en 2005, sur les interventions des États tiers dans les crises sécessionnistes. Nous défendons ici notre méthode et la validité de nos résultats. Nous expliquons aussi pourquoi, selon nous, il est possible de remettre en question les critiques et le devis de recherche de Saideman. Plus précisément, notre réponse se concentre sur sa sélection de cas douteuse et sa mesure des liens ethniques, puisque nous jugeons celle-ci méthodologiquement incorrecte et biaisée.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".