Bibliographic record
Abstract
Abstract This article outlines the approach of the OSCE High Commissioner on National Minorities to matters related to the use of language, assessing its consistency. Language is a major identity marker and as such can become a contentious issue in multi-ethnic societies. Questions revolving around the use of language can catalyse fights around distribution of power within States. They can also become a source of conflict and tensions between States, requiring the attention of international organizations such as the OSCE. Conversely, sound language policies can be instrumental in defusing tensions and strengthen the cohesion of diverse societies. Since its inception thirty years ago, the HCNM has devised a framework for developing balanced language policies as an instrument for conflict prevention. In the HCNM experience, the promotion and use of minority languages needs to be balanced by the adoption and promotion of one or more official languages. The article argues that the HCNM approach relies on the ‘positive’ securitization of linguistic rights, and proves that through its thematic recommendations the HCNM has embarked on a mission of addressing languages and minorities through inclusion and integration, as an approach to build a win-win model of global and regional security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".