Power and The "Everyday Politics" of Refugee Protection in the Case Study of Gioiosa-Ionica, Italy
Bibliographic record
Abstract
The way in which a norm is legally codified in laws and treaties can differ from what that norm actually accomplishes in practice.Norm implementation interrogates how global norms are put into practice at the local level.This thesis analyzes the "everyday politics" of protection implementation through the case study of the SPRAR refugee protection project in Gioiosa-Ionica, Italy.I illustrate how productive and structural power work through the intimate relationship between frontline workers who are formally mandated to implement protection, and the refugees who are the beneficiaries of protection.Frontline workers create new local norms through patterned behaviour and practices that condition legal protection, the provision of basic services and integration measures, on the acquiescent behaviour of refugees.This perpetuates a stereotypical refugee subjectivity based on passiveness and "victimhood".Refugees resist these practices and the way their protection is received in commonplace and concerted ways.Local actors who are not officially mandated condition the arena in which frontline workers do their work by infiltrating implementing organizations; and placing barriers on how they are able to do and accomplish their work.As such the "everyday politics" of refugee protection in Gioiosa-Ionica is fertile ground for how power, resistance and contestation play into norm implementation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".