Explaining Participation in Regional Transnational Social Movement Organizations
Bibliographic record
Abstract
Since the late 1980s, governments have focused intensely on formalizing political and economic relationships within regions. There has also been a concurrent rise in transnational, regional level organizing among social movement activists globally, suggesting the regionalization of `global civil society.' However, opportunities for participation in transnational associations vary widely across countries. In this article, we examine the influence of international (both global and regional) institutional contexts, citizen participation in international society, and national level factors on varying levels of participation in regional transnational social movement organizations (TSMOs). We use negative binomial regression to examine relationships among these factors at three time points: 1980, 1990, and 2000. We find that in the early time period, citizen network connections to international society facilitated the formation of and participation in regionally organized TSMOs. Over time, however, regional and global institutional contexts were more predictive of participation in regional TSMOs than were international network ties. Our analysis also uncovered how qualitatively different forms of regionalism translated into significantly different levels of TSMO regionalization. In Europe, where the regional institutional structure is more elaborated than elsewhere in the world, the number of regional TSMOs in which citizens participated greatly outpaced that found elsewhere. Irrespective of international, institutional factors, however, state-level features remained crucial to explaining the development of regional TSMO sectors and the variable levels of participation in them. Citizens in states with restrictions on political rights and civil liberties had significantly lower participation in these organizations in 1990 and 2000. Even so, over time, citizens in states with more ties to global and regional multilateral processes found more ways to overcome this disadvantage and strengthen their participation in regional, transnational civil society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".