Bibliographic record
Abstract
Abstract This chapter examines the connections and interactions between two organizational strategies: science denialism and greenwashing. The chapter argues that the main difference between these two strategies is that science denialism seeks to undermine stakeholders’ perception of inconvenient scientific facts, whereas greenwashing involves acknowledging such facts while making deceptive claims about one’s environmental performance. The chapter compares the key similarities and differences between the strategies, identifying under which circumstances an organization is more likely to use them simultaneously or choose one over the other. The chapter also provides a list of the main techniques of greenwashing and science denialism and develops a conceptual model illustrating the process that may lead an organization to opt for science denialism, greenwashing, or the making of honest and accurate claims. Finally, the chapter examines several policy instruments that may be used to regulate the information communicated by organizations and mitigate the risks of climate deception, such as advertising restrictions, mandatory disclosure rules, substantiation requirements and independent scientific advisors. Overall, the chapter highlights the importance of studying science denialism and greenwashing as two interlinked organizational strategies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".