Cutting Forever Short: The Impact and Limitations of the Stockholm Convention on Persistent Organic Pollutants (POPs)
Bibliographic record
Abstract
Since their explosion in use during the mid-twentieth century, persistent organic pollutants (POPs) have spread and lingered across the globe, creating lasting adverse effects for both human and environmental health. After decades of national-level regulations fell short of eradicating these chemicals, international governments gathered in 2001 to sign the Stockholm Convention on Persistent Organic Pollutants, aiming to phase out and eventually eliminate the production, use, and transport of these so-called “forever chemicals”. Stockholm’s ultimate goal is to “protect human health and the environment from persistent organic pollutants,” a goal which has not yet been met, twenty-four years after original negotiations. This thesis will use a multidisciplinary approach to examine the original text of the treaty, its design and implementation, and its raw effects in the form of scientific analysis of POP concentration levels. While concentrations were found to be decreasing overall, this improvement is likely attributable to previous regulations more than the work of Stockholm, especially considering Stockholm’s failures of implementation for certain areas of the regime. The thesis will draw on examples (such as regulation of dichlorodiphenyltrichloroethane, or DDT) and make comparisons (to the Montreal Protocol on Substances that Deplete the Ozone Layer) to illustrate these findings.
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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 | 0.003 | 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 | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.011 |
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".