Principles and Guidelines for the Development of a Science-based Decision Making Process Facilitating the Implementation of the 3Rs by Governmental Regulators
Bibliographic record
Abstract
National government establishments, including regulatory agencies, are part of national science systems, which are a subset of larger national innovation systems. The forces that have influenced the basic roles of national government establishments in five countries over the past 10 yr are reviewed on the basis of a study commissioned by Industry Canada in 1999 for the Council of Science and Technology Advisors to the Canadian government. The most significant response among countries has been found to be the financial and managerial restructuring of government science and technology establishments driven by the push toward self-financing. Trends in the roles of government science and technology in six areas are also ranked, with increasing the health and safety of the nation, providing support to industry and participating in international cooperation occupying the first three positions. Potential ramifications of these trends on the implementation of the concept of replacement, reduction, and refinement (the 3Rs) in ensuring the ethical use of animals in regulatory testing are discussed. Science advice and its role in governmental decision making is reviewed on the basis of the 1999 Report of the Council of Science and Technology Advisors, Science Advice for Government Effectiveness. Six principles developed for the effective use of science advice in making regulatory decision have been adapted and are proposed with accompanying guidelines as a framework for the development of a science-based decision making process to facilitate the implementation of the 3Rs by governmental regulators. The proposed series of principles and guidelines are designed to reflect the rapidly evolving and complex context for government decision making, and to remain consistent across government departments and across nations.
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 | 0.286 | 0.238 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.015 | 0.015 |
| Research integrity | 0.039 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 0.009 |
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".