Local impacts, global sources: The governance of boundary-crossing chemicals
Bibliographic record
Abstract
Over the last half century, a multijurisdictional, multiscale system of governance has emerged to address concerns associated with toxic chemicals that have the capacity to bioaccumulate in organisms and biomagnify in food chains, leading to fish consumption advisories. Components of this system of governance include international conventions (such as the Stockholm Convention on Persistent Organic Pollutants and the Minamata Convention on Mercury), laws enacted by nation states and their subjurisdictions, and efforts to adaptively manage regional ecosystems (such as the U.S.-Canadian Great Lakes). Given that many of these compounds - including mercury, industrial chemicals such as polychlorinated biphenyls, and pesticides such as toxaphene - circulate throughout the globe through cycles of deposition and reemission, regional efforts to eliminate the need for fish consumption advisories cannot be successful without efforts to reduce emissions everywhere in the world. This paper argues that the scientific community, by monitoring the concentrations of these compounds in the atmosphere and by modeling their fate and transport, play an important role in connecting the various jurisdictional scales of governance. In addition, the monitoring networks that this community of scientists has established can be visualized as a technology of governance essential in an era in which societies have the capacity to produce and release such chemicals on an industrial scale.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".