Faultline Citizenship: Ethnonational Politics, Minority Mobilisation, and Governance in the Israeli “Mixed Cities” of Haifa and Tel Aviv-Jaffa
Bibliographic record
Abstract
Belfast, Jerusalem, Brussels, Montreal, Sarajevo and Nicosia are among the most oft-mentioned examples of ethnically divided cities, situated in a wider context of ethnonational conflict (to varying degrees of intensity). At the same time there are other cities where ethnic and cross-community tensions are significant, but which have not occupied sufficient academic interest. In the Israel/Palestine context, the cities of Tel Aviv-Jaffa and Haifa represent such cases. They both contain a minority of Israeli-Palestinians whose patterns of political mobilization and interaction with local state institutions have rarely been explored. Yet the interaction between urban governance actors and Palestinian activists in these cities reveals much about the nature of contemporary ethnopolitics in Israel/Palestine. The aim of the paper is to provide an analysis of the ways through which the different mobilization strategies of Israeli-Palestinians in these cities are shaped by altercations between local governance mechanisms, and the internal and external intricacies of ethnic movement politics. The paper develops a relational approach to the study of citizenship in ethnically polarized cities. It suggests that powerful insights into patterns of claiming citizenship can be gained by incorporating dynamic institutional approaches to local minority mobilization with the important roles of symbolic urban politics and the politics of place. The constellation of those factors provides for a rich picture of the subtleties of minority strategies and the governance of ethnically fractured cities.
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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 | 0.001 | 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 | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".