GREENWASHING: A PROPOSAL TO RESTRICT ITS SPREAD
Bibliographic record
Abstract
Recent years have seen a rapid rise in the number of firms publicly touting the environmental merits of their products or their operational practices. This is driven by the changing societal concern and public discourse around environmental issues. What was once an infrequent conversation has emerged as a moral obligation. While the number of "green" products available in the market has grown, these conditions have also resulted in firms deliberately misleading consumers about their environmental performance or the environmental benefits of their products, a condition commonly known as "greenwashing." This paper will argue that the urgency to address our rapidly deteriorating environment requires that tangible steps be taken to control incidents of greenwashing. It examines the merits and drawbacks associated with government's taking an active role in the regulation of greenwashing and argues that the current regulatory instruments being used by governments to address greenwashing are not likely to be successful in addressing the problem. Finally, the paper proposes a new regulatory instrument where governments and interested stakeholders work together to collect and disseminate information on sustainable business practices and the impact of goods and service production on the environment.
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 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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.039 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".