Inclusion, exclusion or indifference? Redefining migrant and refugee host state engagement options in Mediterranean ‘transit’ countries
Bibliographic record
Abstract
What determines policies toward migrants and refugees in the transit-turned-host countries? Compared to the vast literature examining migration to Europe and North America, we know relatively little about why ‘newer’ host states pursue a liberal strategy with access to residency, employment and services on par with citizens, or what drives them to treat migrants and refugees with exclusion. This paper argues that there is a third choice: the idea of indifference-as-policy. Indifference refers to indirect action on the part of the host state, whereby a state defers to international organisations and civil society actors to provide basic services to migrants and refugees. The paper uses data collected over two years in Egypt, Morocco and Turkey to examine how this tripartite understanding of engagement maps onto empirical reality. Drawing on this analysis, the argument in this paper is two-fold. First, indifference is a strategic form of engagement utilised by host states, and that it creates a specific type of environment that allows for the de facto integration of migrants and refugees. Second, even when host states take steps toward a more liberal engagement strategy, examining policy outcomes, rather than outputs, demonstrates that indifference is still the dominant policy.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".