Bibliographic record
Abstract
Minority Rights, Jennifer Jackson Preece, Cambridge (UK) and Malden MA: Polity Press, 2005, pp. ix, 213. This book is not, as its title might be thought to suggest, an abstract conceptual analysis of a particular sub-set of rights. Although it builds on, and acknowledges, the work of Kymlicka, Raz, Taylor and Shklar (160), the narrative thread that gives it unity is historical. It deepens our understanding of the nature of the discourse of minority rights by contextualizing that discourse both temporally (through historical examples) and spatially (through adroitly selected comparative examples). With extraordinary succinctness and clarity the author guides us through a succession of political epochs: the time of the Christian and Islamic medieval universitae, the period of the dynastic re-organization of Europe, the modern era of popular sovereignty with its attendant notions of civic and ethnic nationalism, and especially the contradiction-laden time of European imperialism and its post-imperial and post-colonial reverberations. As this narrative unfolds we follow the vicissitudes of religious, racial, linguistic and ethnic minorities and observe the successive forms taken by the “problem of minorities.”
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.023 |
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".