Discovering Greenwashing Behavior Patterns: A Systematic Literature Review Approach
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
<p>As technology advances and environmental awareness grows, the ESG concept has deeply influenced society and businesses. However, some companies, while promoting sustainability commitments, evade their actual responsibilities, leading to the rise of greenwashing. This behavior undermines genuine efforts and fosters public distrust, potentially causing as much harm as environmental pollution. This study proposes a machine learning approach, using a Systematic Literature Review to analyze recent research on greenwashing. The analysis highlights key research trends across various fields and identifies significant growth in 2022 and 2023. It ultimately uncovers essential features of corporate greenwashing and commonly used tactics, aiming to provide a framework for intelligent detection of greenwashing.</p> <p>&nbsp;</p>
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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