Chemical Regulation: Approaches in the United States, Canada, and the European Union
Bibliographic record
Abstract
Correspondence issued by the Government Accountability Office with an abstract that begins "Chemicals are used to produce items widely used throughout society, including consumer products such as cleansers, paints, plastics, and fuels, as well as industrial solvents and additives. While chemicals play an important role in everyday life, some may be harmful to human health and the environment. Some chemicals, such as lead and mercury, are highly toxic at certain doses and need to be regulated because of health and safety concerns. In 1976, the Congress passed the Toxic Substances Control Act (TSCA) in part to authorize the Environmental Protection Agency (EPA) to regulate chemicals that pose an unreasonable risk to human health or the environment. TSCA addresses chemicals that are manufactured, imported, processed, distributed in commerce, used, or disposed of in the United States and authorizes EPA to assess chemicals before they enter commerce (new chemicals) and review those already in commerce (existing chemicals). TSCA excludes certain chemical substances, including among other things pesticides that are regulated under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA); and food; food additives; drugs; cosmetics or devices that are regulated under the Federal Food, Drug and Cosmetic Act (FFDCA). In this context, Congress asked that we provide comparative information on the following chemical control laws: TSCA, Canadian Environmental Protection Agency (CEPA), the current European Union legislation, and the Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH) as proposed. Specifically, Congress asked that we provide information on the approaches of (1) controlling chemical risks, (2) reviewing existing chemicals used in commerce, (3) assessing new chemicals, and (4) handling confidential business information."
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.028 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".