Keeping up with the Neighbours: Diffusion of Norms and Practices Through Networks of Employer and Employee Organizations in the Eastern Partnership and the Mediterranean
Bibliographic record
Abstract
Abstract Using social network analysis and logistic regression, we analyse how inter‐organizational networks facilitate co‐operation and the transfer of best practice from EU‐based organizations to organizations in the European Neighbourhood Policy (ENP) countries. More specifically, we examine networks of employer and employee organizations that participate in the Civil Society Forum of the Eastern Partnership and in TRESMED, a Mediterranean project. We find that networks are successful at disseminating principles and good practices of economic and social partnership, but also that dissemination proceeds slowly. In addition, we detect more co‐operation among employer than among employee organizations, which reflects collective action difficulties facing organized labour more generally. Last, we find that inter‐organizational co‐operation is more intense in the Southern than in the Eastern neighbourhood, which is explained by contextual differences as well as by the EU's longer‐term engagement with the Mediterranean than with the countries on its Eastern frontier.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".