Introduction: Global Laws, Regulations, and Standards for Animals in Research
Bibliographic record
Abstract
This issue contains a collection of papers describing the state of animal laws, regulations, and standards in counties worldwide, grouped by geographic regions (i.e., North America, South America, Pacific Rim, Africa, and the Middle East). An overview of the US and Canadian legal requirements for animal welfare is provided, along with consideration of potential gaps in the US Animal Welfare Act. The EU Directive on the protection of animals used for scientific purposes and its transposition is discussed, and challenges facing laboratory animal protection regimes in Latin America and the Pacific Rim are examined. Legislation for laboratory animal use has been enacted in India and Australia, while animal protection regimes have not yet been enacted in the Middle East and Africa. International harmonization is a particularly important challenge for the global scientific community and private accreditation by organizations such as AAALAC International, plays a key role in promoting high standards for animal care and use programs globally. This article highlights three future trends. First, international efforts at harmonization will continue, and seek to keep pace with the globalization of science. Second, nations that have not yet developed robust legal systems applicable to laboratory animal welfare will seek out the expertise of those nations that have well established regimes. Third, for countries with mature animal protection systems, animal use in research will continue to be of societal concern, and efforts to change existing laws will not abate. The opportunity to use animals in laboratory research is not an entitlement. It is a privilege accorded by society to certain members of the scientific community and along with it comes the responsibility to adhere to, and seek improvement in, applicable laws, regulations, policies and standards.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.049 | 0.063 |
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".