Partnership Structure and Partner Outcomes: A Comparative Study of Large Community Sustainability Cross-Sector Partnerships in Montreal, Barcelona and Gwangju
Bibliographic record
Abstract
The aim of this research was to understand the structural features of large cross-sector social partnerships (CSSPs) and their resulting partner outcomes. This study analyzed and compared the partnership structures of three large CSSPs, each from a different continent: Barcelona + Sustainable in Barcelona, Spain; Gwangju Council for Sustainable Development in Gwangju, South Korea; and Sustainable Montreal in Montreal, Canada. Based on a survey of the partners in each of the three partnerships, the partner outcomes were also determined and compared. Building on these findings and using abductive analysis, the relationships between the partnerships’ structural features and partner outcomes are considered. An updated set of seven structural features for studying large cross-sector partnerships is offered. The empirical findings show some differences between the partnership designs and between the partner outcomes of the three partnerships. The experiences of the civil society, private sector and public sector partners in each of the cases were relatively similar, showing that in large partnerships, the sector was less relevant than in small partnerships.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".