Explaining the Selection of Routines for Change during Organizational Search
Bibliographic record
Abstract
We examine how organizations select some routines to be changed, but not others, during organizational search. Selection is a critical step that links an exogenous trigger for change, change in individual routines, and larger processes of organizational adaptation. Drawing on participant observation of an initiative to improve perioperative efficiency in seven Ontario hospitals, we find that organizational roles shape selection by influencing both politics and frames in organizational search. Roles shape politics by defining the role-specific goals of the people who have authority to change a routine. Organizations will not select a routine for change unless at least some elites—people with role-based authority—frame the existing routine as negatively affecting their role-specific goals. Roles also shape individuals’ frames. Because people are only partially exposed to interdependencies between routines in their day-to-day work, they may not be fully aware of the diverse impact that an existing routine can have on their goals. Proponents for change can use strategic framing to focus attention on interdependencies between routines to get elites to better see how an existing routine negatively affects their goals. They can also change elites’ goals by using strategic framing to focus attention on new and broader goals that the change in routine would promote.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".