Bureaucrats as policy makers: Minority accommodation and exclusion in ethnic nation‐states
Bibliographic record
Abstract
Abstract Why do some ethnic nation‐states employ simultaneously inconsistent policies toward ethnic minorities, accommodating them in one sphere but excluding them from another? This article argues that this incoherent treatment of minorities results from the delegation of authority to bureaucrats. Building on the principal‐agent approach and nationalism scholarship, this work develops a causal mechanism explaining the conditions under which bureaucrats can shift state policies toward ethnic minorities and the types of policy preferences they seek to materialize. Defining policy problems, strategically framing optimal solutions, and building support for policy initiatives are the mechanisms through which nonelected officials challenge the existingstatus quoand seek to shift state policies toward ethnic minorities. This theoretical framework is applied to education and land policies in Israelvis‐à‐visthe Palestinian Arab citizens to illustrate how entrepreneurial bureaucrats can bring about diverse policy outcomes toward the same minority. Related Articles Ben‐Bassat, Avi, and Momi Dahan. 2018. “Biased Policy and Political Behavior: The Case of Uneven Removal of Elected Mayors in Israel.”Politics & Policy46(6): 912–50. https://doi.org/10.1111/polp.12280 . Harel‐Shalev, Ayelet. 2009. “Lingual and Educational Policy toward ‘Homeland Minorities’ in Deeply Divided Societies: India and Israel as Case Studies.”Politics & Policy37(5): 951–70. https://doi.org/10.1111/j.1747‐1346.2009.00206.x . Tusalem, Rollin F. 2015. “Ethnic Minority Governments, Democracy, and Human Rights.”Politics & Policy43(4): 502–37. https://doi.org/10.1111/polp.12125 .
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".