Bibliographic record
Abstract
Evidence-based regulation is a term of art that refers to the process of making decisions about regulation based on evidence generated through systematic research. There is increasing pressure to treat evidence-based regulation as a global best practice, including in the area of anti-bribery law. Too little attention has been paid to the fact that under certain conditions evidence-based regulation is likely to be a less appealing method of decision making than the alternative – namely, relying on judgment. Those conditions are: it is difficult to collect data on either interventions or outcomes; accurate causal inferences are difficult to draw; there is little warrant for believing that the same causal relationships will apply in a new context; or the decision makers in question lack the capacity to undertake one of these tasks. These conditions are likely to be present in complex, transnational, decentralized, and dynamic forms of business regulation such as the global anti-bribery regime.
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 | 0.420 | 0.526 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.093 |
| Scholarly communication | 0.027 | 0.044 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.027 | 0.049 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".