Seizing Transnational Opportunities in Times of Political Backlash: The Transformation of Civil Society Organizations’ Activism in Italy
Bibliographic record
Abstract
Abstract This chapter investigates how civil society organizations (CSOs) concerned with migration and human rights issues in Italy pursue their political advocacy in light of a political environment that has become increasingly hostile due the resurgence of populist and nationalist forces. More specifically, the focus of the analysis is on the venues and strategies that Italian CSOs have explored at the transnational level to make their voices heard and to sustain their political campaigns. Based on a set of 28 interviews with CSOs working on human rights-related issues, this chapter considers how opportunities, constraints, and resources have become a relevant dimension of the advocacy of CSOs, specifically in the system of multilevel governance within Europe. We also take into account the fact that the political and legal space of the EU has seen a notable consolidation and opened up a political and institutional space for the mobilization of CSOs in Italy. The analysis demonstrates that, given their specific skill sets, the larger CSOs in particular are in a position to engage in strategic litigation at a European level, to tap into resources accessible through the EU, and to build advocacy networks across different member states. In this respect, CSOs in Italy have increasingly explored the transnational political arena for pragmatic, albeit politically pressing reasons.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".