Bibliographic record
Abstract
Private transnational organizations have grown in number and in influence. However, sociologists and political scientists often study them separately, either as transnational social movement organizations (TSMOs) or the larger category of international non-governmental organizations (INGOs). In this paper, I examine the determinants of TSMO legitimacy by drawing on the literature on INGOs. In so doing, I call for bridging the disciplinary gap between sociology and political science. Empirically, I find that legitimation benefits already prominent organizations more than those that are not. Networking thus helps reproduce the hierarchy among the TSMOs, challenging the earlier notion that TSMOs are horizontally networked. However, I also find that Southern TSMOs are more likely to gain legitimacy than Northern TSMOs once they are visible to their peers. The analysis of TSMOs thus cautions our bias to study Northern INGOs and generalize the findings to INGO population. Overall, my findings reveal that the incentives and strategies that INGO research has documented exist among TSMOs despite their counter-hegemonic ambitions.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".