Governing the gap: Forging safe science through relational regulation
Bibliographic record
Abstract
Abstract Designed to close the ubiquitous gap between law on the books and law in action, management systems locate the standard setting and implementation of regulation within the regulated organization itself. Despite efforts to more closely couple aspirations and performance, the gap re‐emerges because the exigencies of practical action exceed the capacity of system prescriptions to anticipate and contain them. Drawing on data from a six‐year ethnographic study of the creation and implementation of an environment, health, and safety management system, this article identifies relational regulation as the approach used by front‐line managers to govern the gap: keeping organizational activities within an acceptable range of variation close to regulatory specifications. We identify four practices – narrating the gap, inquiring without constraint, integrating pluralistic accounts, and crafting pragmatic accommodations – and three conditions under which actors may develop a sociological orientation to enact relational regulation. Overall, the article concludes that the mechanism for assuring compliance resides in the apprehension of relational interdependencies rather than the management system per se.
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 | 0.058 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.107 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".