Bibliographic record
Abstract
Abstract Modern consumers are confronted by a growing array of colorful eco-labels on everything from coffee to computers. Yet, not all of these eco-labels are trustworthy. Despite the existence of well-established best practices for eco-labeling, many labels remain little more than superficial exercises in “greenwash.” How can consumers separate greenwash from genuine attempts to address environmental challenges? Beyond Greenwash? systematically investigates the credibility of transnational eco-labeling in a global and cross-sectoral context. It brings original data, an innovative mixed-method research design, and a unique measure of procedural credibility in transnational governance to bear on one of the most salient questions in contemporary global environmental politics. In doing so, it reaches the important conclusion that the rigor and credibility of transnational governance depends as much on who is being governed as who is doing the governing. Beyond Greenwash? offers practical insights into the viability of eco-labeling as a form of transnational governance and makes a timely contribution to broader debates in political science and international relations about when and how emergent forms of transnational governance can succeed in achieving their stated objectives.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".