Outcomes to Partners in Multi-Stakeholder Cross-Sector Partnerships: A Resource-Based View
Bibliographic record
Abstract
The prevalence and complexity of local sustainable development challenges require coordinated action from multiple actors in the business, public, and civil society sectors. Large multi-stakeholder partnerships that build capacity by developing and leveraging the diverse perspectives and resources of partner organizations are becoming an increasingly popular approach to addressing such challenges. Multi-stakeholder partnerships are designed to address and prioritize a social problem, so it can be challenging to define the value proposition to each specific partner. Using a resource-based view, this study examines partner outcomes from the perspective of the strategic interest of the partner as distinct from the strategic goal of the partnership. Based on 47 interviews with representatives of partner organizations in four Canadian case studies of community sustainability plan implementation, this article details 10 resources partners can gain from engaging in a multi-stakeholder partnership.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 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".