Migrant Advocacy under Austerity: Transforming Solidarity in the Greek-Refugee Regime
Bibliographic record
Abstract
Abstract With the United Nations High Commissioner for Refugees’ declaration of an emergency in 2016, Thessaloniki’s refugee regime began to develop rapidly. Local non-governmental organizations (NGOs) were transformed from underfunded initiatives to institutions managing large budgets and requiring staff with intercultural competencies and experience with migrant populations. For this, they turned to the ‘lost generation’— Greek youth who had come of age during the economic crisis and who had formed close relationships with migrants through political mobilizations and local solidarity networks that had sprung up in response to widespread austerity. In this article, I ask: How is the conversion of young ‘radicals’ from ‘anti-state’ activist networks into NGO representatives achieved? How does this transition impact former activists’ relationships with migrants and how do former activists make sense of this change? What, if anything, makes this a ‘Thessaloniki story’? My argument is three-fold. First, the conversion of activists into NGO representatives is made possible by a combination of young people’s exclusion from economic opportunities and the state’s disciplining of forms of solidarity that exist outside of the refugee regime. Second, the refugee regime converts solidarity established through political struggle into a form of human capital that affords former activists social mobility. Existing solidarity between former activists and migrants is undermined as a result. Third, on account of their new role as gatekeepers in NGOs, former activists struggle to offer a compelling narrative of continuity with their left-wing identity and their politicized commitment to migrants. In doing so, they rely on identifiable forms of boundary work and justification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".