Draft analysis of how networks of international, national and local actors collaborate to reduce vulnerabilities on Six Sites in Europe, Canada, and South Africa
Bibliographic record
Abstract
Based on fieldwork conducted in selected migration arrival ports in Greece, Italy, Spain, France, South Africa, and Canada, this research report explores the role of the notion of vulnerability in the field level governance of international protection. Specifically, it explores how key actors and stakeholders in the selected field sites 1) understand and apply the notion of vulnerability, and 2) collaborate to address and reduce vulnerabilities. Particular attention is paid to how field level governance takes into account gender and legal status, and how actors collaborate in regard to mechanisms for identification, access to legal information and assistance, and access to healthcare and shelter. The field level is where global, regional, national and local actors and stakeholders interact in order to implement the international protection regime. A great variety of actors and stakeholders are as such engaged in the field level governance of migration and refugee protection, including notably government agencies, local authorities, civil society organizations, host community members, and migrants. Critically, our research provides opportunities to explore de facto governance responses and how they do or do not reflect formal governance processes - including national legislative frameworks and the global compacts on refugees and migration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".