Comparing Environmental Policies to Reduce Pharmaceutical Pollution and Address Disparities
Bibliographic record
Abstract
Pharmaceutical products, including active pharmaceutical ingredients and inactive ingredients such as packaging materials, have raised significant concerns due to their persistent input and potential threats to human and environmental health. Discourse on reducing pharmaceutical waste and subsequent pollution is often limited, as information about the toxicity of pharmaceuticals in humans is yet to be fully established. Nevertheless, there is growing awareness about ecotoxicity, and efforts to curb pharmaceutical pollution in the European Union (EU), United States (US), and Canada have emerged along with waste disposal and treatment procedures, as well as growing concerns about impacts on human and animal health, such as through antimicrobial resistance. Yet, the outcomes of such endeavors are often disparate and involve multiple agencies, organizations, and departments with little evidence of cooperation, collaboration, or oversight. Environmental health disparities occur when communities exposed to a combination of poor environmental quality and social inequities experience more sickness and disease than wealthier, less polluted communities. In this paper, we discuss pharmaceutical environmental pollution in the context of health disparities and examine policies across the US, EU, and Canada in minimizing environmental pollution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".