Correlates of rigorous and credible transnational governance: A cross‐sectoral analysis of best practice compliance in eco‐labeling
Bibliographic record
Abstract
Abstract The number of eco‐labeling schemes is rising dramatically, yet the rigor and credibility of such schemes remains uneven. Whereas some eco‐labeling organizations (ELOs) comply with best practice guidelines designed to increase the credibility of their standards through attention to good operating principles, such as transparency and impartiality, others do not. Within this article, I attempt to explain this variation through multivariate regression analysis of an original cross‐sectoral dataset of transnational ELO policies and practices. I find compelling evidence to suggest that ELOs with environmental non‐governmental organization (ENGO) partners, nonprofit structures, or broad transnational reach are most likely to comply with best practices. I also find that private ELOs are more likely to disregard best practices than public ones. Conversely, I find little evidence that levels of industry funding or sector‐specific competition dynamics affect best practice compliance. This study contributes new data, a new method of comparison, and new findings to the growing literature on transnational governance.
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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 | 0.013 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".