Mutual benefit and status quo processes as governance mechanisms in partnerships between organisations that belong to different sectors and organisational models
Bibliographic record
Abstract
Co-operatives are member-owned organisations that follow a set of Co-operative Principles. When they partner with non-co-operative organisations, they risk compromising those principles. However, when partner organisations share those principles in their approaches or aspirations, the partnership generates mutual benefit. Mutual benefit can act as a governance mechanism for the partnership; it promotes co-operation and co-ordination by bringing partners together, maintaining a cohesive strategic direction, and promoting a common vision of the future. Concern for the community, for example, is a co-operative principle but it is also a typical approach among non-co-operatives and the alignment can support the partnership. Close alignment generates a high level of mutual benefit, while broad alignment generates a low level and therefore acts as a weaker mechanism. This article examines the role of mutual benefit in partnerships between the healthcare co-operative Saskatoon Community Clinic (SCC) and several University of Saskatchewan colleges, schools, departments, and divisions. Through these partnerships, the SCC hosts healthcare clinics, specialist healthcare services, and student placements, and generates research. In these cases, co-operation and co-ordination are either supported by a high level of mutual benefit, rely on an available status quo procedure, or are minimised by low interdependence between partners.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".