Transnational Ideas and Connections: Understanding Asian Civil Society Activism
Bibliographic record
Abstract
Whether one looks back at armed insurgency movements, the Philippines’ People Power, or Jakarta’s riots against Suharto, transnational ideas, models of collective action, and activists have been keys in inspiring and fostering civil society mobilization and organizations in Southeast Asia. What are some of the common characteristics of Asian civil society activism, and what are some of the differences? Can we explain these similitudes and differences across countries, especially within Southeast Asia? Are there themes for activism that are more dominant than others? To answer these questions, we first undertook a short historical and comparative review of social activism in the region before conducting a preliminary analysis of a database on NGOs, networks, and coalitions in various Southeast Asian countries. Our results seem to show that national organizations tend to be influenced by agenda setting on the part of regional organizations, to the point where it might trump the importance of national/local issues, such as the regime type, and might homogenize the issues on which organizations work across countries. At the same time, national/local animosities also influence regional organizations, whether they want it or not. In sum, regional and national organizations shape each other, and that the influence is far from going only in one single direction.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".