Beyond Differentiation and Integration: The Challenges of Managing Internal Complexity in Federations
Bibliographic record
Abstract
In this paper we examine the management of internal complexity in federations as a means of shedding new light on how the challenges inherent in governing these forms of inter-organizational networks are managed. Our analysis reveals that these networked organizations differed as a function of their approach to four complexity management activities: perspective shifting, shaping interactions, managing standards and constructing commitment. Based on the use of these four activities we identify three approaches to complexity management in this study – leveraging complexity, suppressing complexity and disengaging from complexity. Each of these approaches differed in their focus on differentiation or integration in the implementation of complexity management activities. We found that only leveraging complexity went beyond separate management activities aimed at differentiation or integration and employed policies and activities that possessed the capacity to optimize both simultaneously. In doing so, our study highlights new possibilities for complexity management by revealing the ways in which management activities can be designed to optimize both integration and differentiation.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".