Protecting the Paradox of Interprofessional Collaboration
Bibliographic record
Abstract
We studied an interprofessional collaboration to understand how professionals engaged with paradox in collective decision-making. At the beginning of our study, we observed vicious cycles in which conflict led to negative tension. Professionals were holding tightly to a particular pole of the paradox, and the higher-status pole was consistently overrepresented in collective decision-making. By the end of our study we observed the presence of virtuous cycles, where conflict led to more positive tension, and where professionals engaged in collective decision-making with more equal representation of conflicting approaches. We call this change process protecting the paradox and we identify three strategies that support this process: (1) promoting equality of both poles, (2) strengthening the weaker pole, and (3) looking beyond the paradox by focusing on desired outcomes. We contribute to the paradox literature by showing how vicious cycles can be shifted to virtuous cycles, how professionals and managers can work together to protect a paradox, and how status differences between poles can be redistributed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".