Ethnic challenges to the modern nation state
Bibliographic record
Abstract
List of Contributors Introduction S.Ben-Ami PART ONE: COLLECTIVE IDENTITY, INDIVIDUAL FREEDOM AND THE MODERN STATE: THEORETICAL CONSIDERATIONS Modernity and National Identity W.Kymlicka Towards the Rehabilitation of 'Nation Building' and the Reconstruction of Nations R.Kook Culture Is Not Enough: A Democratic Critique of Liberal Multiculturalism Y.Peled & J.Brunner PART TWO: ETHNIC STRIFE AND THE STATE OF WESTERN EUROPE Catalan and Basque Nationalism: Contrasting Patterns S.G.Payne Ethnicity, Multiculturalism and Society in Germany M.Zuckerman The New Right and the Intellectual Elaboration of Ethnic Europe: From Racism to the Politics of Differentialism A.Spektorowski PART THREE: ETHNIC CHALLENGES IN SETTLER STATES Minority Protest and the Emergence of Ethnic Regionalism: Palestinian-Arabs in the Israeli 'Ethnocracy' O.Yiftachel Nationalism and Non-racialism in South Africa G.Shafir Rethinking Multinational Space: The Quebec Referendum and Beyond A.G.Gangon Modernity, Premodernity and the Political: The Neozapatistas of Southern Mexico E.Herran Control and Resistance: Two Dimensions of Palestinians' Existence in Israel A.H.Sa'di PART FOUR: ETHNIC CONFLICT IN POST-COLONIAL SOCIETIES The Resurgence of Ethnohinduism - A Theoretical Perspective O.Shani Ethnic Groups and the State in Africa B.Neuberger Islamism and Democracy in Algeria G.M.Munoz Index
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".