Testing the greenwashing assessment framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Greenwashing is of growing concern as the world struggles to respond to the triple planetary crises of pollution, climate change, and biodiversity loss. New terminology to label greenwashing has entered public discourse and new policies and legal processes have challenged green claims, particularly in advertising. These developments demand a review and revision of the terminology used in greenwashing research and analysis of its application to statements made by businesses, governments, and other organizations. This paper focuses on just that, making two key academic contributions to the growing interdisciplinary literature on greenwashing. First, we empirically test, for the first time, the greenwashing assessment framework, an analytical means to assess greenwashing. Second, we build on our empirical findings to propose a revision to this framework. This testing makes an important contribution to help the public, managers, policy makers, and journalists navigate the complex information domain surrounding environmental issues.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it