Şener Aktürk. Regimes of Ethnicity and Nationhood in Germany, Russia, and Turkey.
Bibliographic record
Abstract
In this ambitious comparative study of change in state policies regarding ethnic diversity, Şener Aktürk proposes a tripartite typology of ethnic regimes that he describes as “exhaustive and coherent … [and] theoretically applicable to every country in the world” (p. 43). His ideal types permit him to compare the three different states he examines and to chart change over time. Aktürk also develops a common framework to explain the changes he analyzes, although he does not claim universal applicability for this part of his theory. Aktürk distinguishes among monoethnic, antiethnic, and multiethnic regimes. Monoethnic states offer no official support for minority languages, permit no autonomous territories for different ethnic groups, and make no distinctions among different ethnic groups in identification documents. Monoethnic countries also give priority to the dominant ethnic group in rules governing immigration and access to citizenship. Antiethnic states adopt similar policies with respect to the expression of distinctive ethnic characteristics, but generally promote assimilation, while monoethnic states seek to segregate ethnic minorities. Unlike monoethnic states, antiethnic states give no priority to particular ethnic groups in their immigration and naturalization policies. Multiethnic states are in all these respects mirror images of monoethnic states. They encourage linguistic and other forms of cultural diversity, do not give priority to immigrants from particular ethnic groups, and do not discriminate in the granting of citizenship. Monoethnic and antiethnic regimes are both characterized by intolerance of ethnic difference, while multiethnic regimes seek “multiculturalist accommodation of ethnolinguistic and religious-sectarian diversity” (p. 163).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".