Contested Governmentalities: NGO enrollment and influence over chemical risk governance rationales and practices
Bibliographic record
Abstract
Abstract The assessment and management of chemical risks is a contested domain of governance. Governments are increasingly investing in multi‐stakeholder processes to address thousands of substances that are in widespread use globally, despite never having been assessed for toxicity and exposure risks to human health and the environment. Using a governmentality approach, we examine whether the increased engagement of NGOs is changing how chemical governance is being conducted in Canada. To do this, we focus on a combination of expert subjectivities, knowledge inputs and prevailing risk assessment and management practices and rationales. The advocacy of alternative conduct and approaches by NGOs (e.g. stronger regulations, reductions in production, shifted burden of proof, new knowledge practices, greater transparency of technical details etc.) is situated within relations of power between neo‐liberal states, industry and science. Various ‘enrollment’ tactics shape the influence of NGOs, which explains in part why particular practices gain ascendency over alternatives (e.g. restrictive timelines, contracts with limited funds, information access, questioning of scientific credibility etc.). The influence of NGOs is complex, as they engage within imposed rules for conduct and governance, simultaneously challenging and reinforcing dominant practices and norms. Risk governmentalities and rationales therefore shape not only the conduct of citizens, but also that of governance stakeholders themselves. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.031 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".