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Record W2105703559 · doi:10.1080/17450100701381821

Strategies of Minority Struggle for Equality in Ethnic States: Arab Politics in Israel

2007· article· en· W2105703559 on OpenAlexaboutno aff
Amal Jamal

Bibliographic record

VenueCitizenship Studies · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsEthnic groupCitizenshipPolitical scienceGender studiesPolitical economySociologyLaw

Abstract

fetched live from OpenAlex

This paper challenges existing theories of radicalization and secession that are presented as “natural” tendencies of minority nationalism. It demonstrates the affinity between the strategies of national minorities and those of social movements, claiming that excluded minorities seek to reframe and expand the meaning of their citizenship, as do social movements, by utilizing the structures of opportunities available to them through citizenship and by mobilizing whatever resources possible to improve their status. Minorities utilize the opportunities embedded in their citizenship, despite its shortcomings, before ever moving to alternative strategies that may jeopardize the valued incentives that were achieved so far as citizens. The paper demonstrates its theoretical hypothesis by examining the changes taking place in the strategy adopted by the Arab minority in Israel. This minority has chosen to abandon accommodative politics and is adopting a more active and challenging strategy vis-à-vis the state. In contrast with common claims that conceive Arab politics as a tendency towards strategies of radicalization and confrontation with the state, this paper demonstrates that recent changes in Arab politics seek to expand the meaning of citizenship beyond liberal limits and adapt it to new conditions in order to meet the minority's expectations of full and equal citizenship.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.010
Scholarly communication0.0090.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.171
GPT teacher head0.426
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2007
Admission routes1
Has abstractyes

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