Corporate ‘Capture Strategies’ Impacting Human and Ecosystem Health
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The concept of regulatory capture has been extensively studied in academic literature, primarily within the social sciences. This phenomenon has been increasingly discussed in the environmental sciences as the impacts of regulatory capture on human and ecosystem health have become increasingly apparent. Regulatory capture is just one tactic employed by vested interests in the strategy of delaying, weakening, or abolishing policies designed to protect the public interest. Here, we define capture strategies as ‘the act of influencing individuals, organizations, or governments to prioritize corporate interests over those of human and ecosystem health’. Similar to the evolution of terms like whitewashing and greenwashing into the broader concept of colorwashing, this new definition expands the scope of capture to include a wide range of targets, such as individuals, educational institutions, nongovernmental organizations, media, and local, national, and intergovernmental organizations. By broadening the definition, we anticipate that researchers, policymakers, and civil society will find it easier to identify and prevent such nefarious activities. This paper illustrates how ‘capture strategies’ have played, and (unless kept in check) will continue to play, an instrumental role in obstructing efforts to address the triple planetary crises of climate change, biodiversity loss, and chemical pollution.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".