Merging Institutional Logics and Negotiated Culture Perspectives to Help Cross‐Sector Partnerships Solve the World's Most Wicked Problems
Bibliographic record
Abstract
Showcasing a sixteen‐month ethnographic study of a coalition to end homelessness in Western Canada, we show how the integration of two theoretical perspectives—institutional logics and negotiated culture—can be used as complementary, yet distinct lenses to better inform the practice of cross sector partnerships which tackle the world's wicked problems. In doing so, we highlight how we were able to holistically capture the meaning systems at work in such multi‐faceted partnerships resulting in a better understanding of how partnerships can work across difference to affect positive social change. In particular, we capture how multiple stakeholders make sense of a partnership's identity in a variety of different ways based upon meaning systems with which they identify at multiple levels as well as how they enact bridging skills across meaning‐related boundaries to promote more effective partner interface.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".