Exploring Transnational Advocacy Networks for Environmental Sustainability
Bibliographic record
Abstract
This study aims to explore papers and assess how they have been addressing TAN features to understand better and explore a structure for the effectiveness of transnational advocacy networks (TANs) for environmental sustainability. Based on data collected, papers on the thematic of transnational advocacy networks for the environment were selected and explored to understand better what features are shown and under what light. Transnational advocacy networks for environmental issues are common in the literature, as the topic draws the attention of nongovernmental organizations. Many of the papers explore at least one of three pillars among the results, and frequently more than one is brought up into theoretical and empirical discussion. These results highlight specific features among each of the characteristics, building a framework so that TANs may have a path to structure their activities to achieve their goals more effectively. Further studies may advance this knowledge in practice. This study seeks to contribute to the existing literature from a theoretical perspective, integrating and exploring the dimensions of transnational advocacy networks and considering a possible structure to improve their results.
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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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".