Editorial: Special issue on protecting and including ‘new’ and ‘old’ minorities
Bibliographic record
Abstract
Questions concerning the rights of minorities and the preservation of social cohesion in ethnically diverse societies are among the most salient on the political agenda of many States. The growing diversity of national communities has generated pressures for States to create and adopt new models to accommodate diversity. Migration is becoming an increasingly important reality for many sub-national autonomous territories where traditional-historical groups (the so-called ‘old minorities’) live, such as Catalonia, South Tyrol, Scotland, Flanders, the Basque Country, and Quebec. Some of these territories have attracted migrants for decades, while others have only recently experienced significant migration inflows. The presence of old minorities makes the management of migration issues more complex.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.018 |
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".