Deconstructing Complexity: How Organizations Cope with Multiple Institutional Logics
Bibliographic record
Abstract
We present a framework that deconstructs institutional complexity and articulates the range of hybrid arrangements that organizations adopt to cope with multiple institutional demands. The framework highlights three factors that contribute to the experience of complexity – namely, the extent to which the prescriptive demands of logics are incompatible, whether there is a settled or widely accepted prioritization of logics within the field, and the degree to which the jurisdictional claims of the logics overlap. Our central thesis is that these ‘components’ of complexity variously combine to produce four distinct institutional landscapes, each with differing implications for how organizations might respond. We explore the situational relevance of an array of hybridizing responses and discuss their implications for organizational legitimacy and performance. We conclude by specifying the boundary conditions of the framework and highlighting fruitful directions for future scholarship.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 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".