A new paradigm for regulatory sciences
Bibliographic record
Abstract
Regulatory science, rooted in legal requirements, provides a mechanism for identifying, assessing, and managing harm to humans and the environment from exposure to hazardous substances. A challenge for regulatory authorities is that many governing laws reflect the scientific paradigm of the mid-20th century. By the nature of legislative processes, most laws are not able to readily adapt to incorporate scientific advances that are inherent in an ever-evolving paradigm. Consequently, the issue of rigid legal frameworks has become prominent in global discussions related to the incorporation of reliable and relevant modern technology to fulfill regulatory needs. To explore this issue, we apply Thomas Kuhn's The Structure of Scientific Revolutions as a conceptual framework to help understand the natural progression of scientific paradigms (from normal science, to anomaly, to crisis, to revolution, and finally to a new normal), identify where we are now in the paradigm cycle, and to explore a path towards a revolution that enables timely implementation of the best available science to fulfil legal requirements.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".