An Integrated Framework to Assess Greenwashing
Bibliographic record
Abstract
In this paper we examine definitions of ‘greenwashing’ and its different forms, developing a tool for assessing diverse ‘green’ claims made by various actors. Research shows that significant deception and misleading claims exist both in the regulated commercial sphere, as well as in the unregulated non-commercial sphere (e.g., governments, NGO partnerships, international pledges, etc.). Recently, serious concerns have been raised over rampant greenwashing, in particular with regard to rapidly emerging net zero commitments. The proposed framework we developed is the first actionable tool for analysing the quality and truthfulness of such claims. The framework has widespread and unique potential for highlighting efforts that seek to delay or distract real solutions that are urgently needed today to tackle multiple climate and environmental crises. In addition, we note how the framework may also assist in the development of practices and communication strategies that ultimately avoid greenwashing.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".