Transparency in transnational governance: The determinants of information disclosure of voluntary sustainability programs
Bibliographic record
Abstract
Abstract The rise of “new” transnational governance has intensified debates about a lack of accountability in global politics. Reviewing the mechanisms through which transparency can foster accountability beyond the state, this article explores the determinants of information disclosure in the field of transnational sustainability governance. Examining the institutional design of 113 voluntary sustainability programs, we find a positive correlation between the involvement of public actors and information disclosure. In contrast, the role of civil society is more ambiguous. There is no statistical support for arguments linking non‐governmental organization participation to increased transparency. At the same time, our analysis reveals a robust correlation between civil society‐led metagovernance and information disclosure. Moreover, we find that crowding has a negative effect on transparency, whereas normative peer pressures have no influence. At a broader level, the analysis reveals a lack of “deep transparency” among transnational sustainability governors. This limits the scope for transparency‐induced accountability in this policy domain.
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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 | 0.011 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".