Civil Society Actors as Catalysts for Transnational Social Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the roles of transnational civil society organizations and networks in transnational social learning. It begins with an investigation into social learning within problem domains and into the ways in which such domain learning builds perspectives and capacities for effective action among domain organizations and institutions. It suggests that domain learning involves problem definition, direction setting, implementation of collective action, and performance monitoring. Transnational civil society actors appear to take five roles in domain learning: (1) identifying issues, (2) facilitating voice of marginalized stakeholders, (3) amplifying the importance of issues, (4) building bridges among diverse stakeholders, and (5) monitoring and assessing solutions. The paper then explores the circumstances in which transnational civil society actors can be expected to make special contributions in important problem domains in the future.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it