Bibliographic record
Abstract
Divergences in Regulatory Practices and Reform ProposalsA striking, even disconcerting, feature of the evolution and regulation of the various CAMs is the lack of anything approaching consistency of approach across jurisdictions and categories of CAMs.Indeed, a range of regulatory options have found favour in Western jurisdictions, with little evidence of convergence on a dominant paradigm.Table 2.11.Regulation of herbal medicinal products, selected jurisdictions Jurisdiction Regulatory approach Canada Fall under the category of "natural health products" and are subject to a similar approval process.United States Fall under the category of "dietary supplements" subject to lower scrutiny than over-the-counter medicines.United Kingdom Sales and products regulated at the EU level since 2011 (see below); this might be subject to change on account of Brexit. Australia• Regulates as therapeutic substances through a two-tiered system categorized on the basis of risk, requiring products to be either registered or listed prior to sale.• Higher-risk medicines, including WHM, can be sold only after being registered with the Australian Register of Therapeutic Goods (ARTG), pursuant to which each product is individually evaluated for quality, safety and efficacy.71 Traditional use as proof of safety or efficacy is accepted to a very limited extent.• Lower-risk medicines comprising pre-approved, low-risk ingredients and making limited claims are listed on the ARTG, and are not subject to the same individualized scrutiny as higherrisk medicines.Europe • Prior to sale, all herbal medicinal products must obtain market authorization or be registered under the Traditional Herbal Registration process.72 • Efficacy and safety must be substantiated, although, unlike biomedicines, they may be validated through traditional historical use since it was recognized that many herbal products would be unable to fulfil the evidentiary requirements imposed on biomedicine.73
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".