From Logic Acceptance to Logic Rejection: The Process of Destabilization in Hybrid Organizations
Bibliographic record
Abstract
We study the introduction of the private logic into a mature Italian hospital that was governed previously as a hybrid of professional and public logics. Intriguingly, the reconstituted hospital was for several years widely praised for its strong clinical and financial performance, but quickly and with little warning, it became riven by political differences that led to its demise. Through our case analysis, we develop a multilevel model that reveals the destabilizing process that can unfold when a new logic enters an established organization. We contribute to the hybrids literature by explaining the puzzle of how a new logic can become accepted and then rejected in organizations, emphasizing the critical importance of the interaction between the audience, organization, and practice levels. Crucially, we reveal that positive feedback from multiple audiences may be a mixed blessing for hybrids: although it offers resource and legitimacy advantages, it can induce internal tensions with severe destabilizing consequences. Our findings and model also run counter to two core assumptions within the institutional literature: that social endorsement is advantageous and that alignment with institutional expectations results in stabilization. We qualify these assumptions and indicate the circumstances under which they are unlikely to hold.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".