Anxiety, Ambivalence, and the Violence of Expectations: Migrant Reception and Resettlement in Sicily
Bibliographic record
Abstract
Throughout Europe, the arrival of irregular migrants in recent years has triggered the expression of nativist anxieties, witnessing a broad recourse to violent rhetoric and the embrace of exclusivist models of national and regional community. With an ethnographic focus on migrant resettlement in Sicily, this paper argues that the elaboration of a cosmopolitan ethic that rejects the politics of exclusion can be met with ambivalence by local people who share neither the middle-class sensibilities of refugee advocates, nor their access to the public funds by which it is possible to earn income as social service and resettlement workers. Consequently, migrant advocacy is dogged by the widespread presumption that its aims are not necessarily altruistic, and that the apparatus of migrant resettlement is staffed by actors best positioned to take advantage of the lucrative opportunities presented by the migrant crisis. At the same time, newcomers’ incorporation into the body politic as a reserve army of labour, and the expectation imposed upon them to adapt to host communities and contribute productively to the local economy in any way they can, represents a key element of migrant marginalisation that even their own advocates do not explicitly question. Finally, this marginalisation is further exacerbated by contemporary populist rhetoric that positions newcomers as an existential threat to the society.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".