The Unfolding of Control Mechanisms inside Organizations: Pathways of Customization and Transmutation
Bibliographic record
Abstract
Organizational control is a fundamental function of all organizations. Drawing on ethnographic data from one hospital implementing a new behavioral control mechanism across multiple internal units, I explore how control mechanisms spread and unfold inside organizations. This study shows that control mechanisms are co-created through interactions between managers and employees as they engage in an iterative team learning process in two stages: (1) learning about the mandated control mechanism in order to assess its viability in their local context; and (2) learning how to (re)design the control mechanism so that it delivers its intended control outcomes. It also identifies two pathways through which control mechanisms unfold. Along the customization pathway, teams customize the mandated control mechanism so that it functions well in their context. Along the transmutation pathway, teams develop their own locally designed alternative control mechanism to achieve the intended control outcomes based on their own assessment of their unit’s problems. By showing how organizational control mechanisms are co-created by management and employees, this study provides a dynamic view of how control mechanisms spread and unfold within organizations.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".