Diaspora Exclusion in Divided Home States: Israel and Turkey Compared
Bibliographic record
Abstract
This article examines how home states define and redefine membership within ‘their’ diaspora and how certain groups and individuals are excluded from this conception through discourse, policy and practice. We argue that ontological security, or the state’s need for a stable sense of identity, coherence and continuity, involves a consistent narrative about belonging that is directed at emigrants and their descendants and shapes how states interact with ‘their’ diasporas. The theoretical literature typically portrayed diaspora engagement in positive and inclusionary terms. Recently, however, scholars have argued that diaspora engagement may also have negative and exclusionary dimensions in the form of marginalisation, securitisation and persecution of specific groups and individuals abroad, as well as the cooptation of other groups. Such practices are likely in the context of divided societies, mirroring domestic modes of exclusion. Our comparative study of Israel and Turkey reveals such exclusionary dynamics based on ethnicity, religion and political inclination, which not only characterise both countries’ domestic contexts but also extend to their overseas populations. Our findings suggest that in divided states, where identity struggles and uneven citizenship shape politics and society, diaspora policies claiming to tie emigrants and co-ethnics to the homeland may never achieve full inclusion.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".