Governing Inside the Organization: Interpreting Regulation and Compliance
Bibliographic record
Abstract
Looking inside organizations at the different positions, expertise, and autonomy of the actors, the authors use multisite ethnographic data on safety practices to develop a typology of how the regulator, as the focal actor in the regulatory process, is interpreted within organizations. The findings show that organizational actors express constructions of the regulator as an ally, threat, and obstacle that vary with organizational expertise, authority, and continuity of relationship between the organizational member and the regulator. The article makes three contributions to the current understandings of organizational governance and regulatory compliance, thereby extending both institutional and ecological accounts of organizations’ behavior with respect to their environments. First, the authors document not only variation across organizations but variable compliance within an organization. Second, the variations described do not derive from alternative institutional logics, but from variations in positions, autonomy, and expertise within each organization. From their grounded theory, the authors hypothesize that these constructions carry differential normative interpretations of regulation and probabilities for compliance, and thus the third contribution, the typology, when correlated with organizational hierarchy provides the link between microlevel action and discourse and organizational performance.
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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 | 0.029 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".