Positively deviant networks: what are they and why do we need them?
Bibliographic record
Abstract
PURPOSE: This paper aims to report "positively deviant" experiences of three public sector networks seeking to enhance organizational and system level capacities. It is the authors' thesis that the knowledge base concerning the true benefits and pitfalls of networks can be captured and interpreted only through intense, ongoing learning effort embedded in practice on the ground, combined with sustained in-depth observation and collaborative research. DESIGN/METHODOLOGY/APPROACH: The paper describes through case examples why and how different kinds of networks within different jurisdictional contexts and different organizational cultures are being used to enhance the climate for change towards better health care and improved health. The authors describe the contexts, structures, processes and impacts of three "positively deviant" networks. FINDINGS: The network form can provide opportunity for nurturing changes and innovations within large organizational and complex system environments. This opportunity to create additional and different pathways for improved decision making and service provision comes with challenges that should be recognized. PRACTICAL IMPLICATIONS: The authors' experiences indicate that, for networks, a key component of success relates to pulling and pushing at the edges of multiple connections and boundaries in "positively deviant" ways. This pushing and pulling is intrinsically evidence of organizational and intraorganizational learning--in the examples presented--for the improvement of health care and health. ORIGINALITY/VALUE: Other networks can learn from the reported experiences and add their own cases to the empirical understanding of how networks can make a difference; this in turn can help the conceptual and theoretical understanding of them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".