The Role of Institutional Context for Sustainability Cross-Sector Partnerships. An Exploratory Analysis of European Cities
Bibliographic record
Abstract
Institutional contexts influence structures and processes of any organizational system. Most of the research on cross-sector partnerships (CSSPs) has focused on their internal performance, methods, and effectiveness; however, the institutional contexts that allow or inhibit their development have been limitedly assessed. Many local CSSPs address sustainability issues, and this research explores Barcelona + Sustainable’s and Bristol Green Capital Partnership’s institutional contexts at the local, national, and international levels. Interviews were conducted with the leaders of the partnerships and responses were assessed using Scott’s (1995) institutional pillars. Findings show the cultural-cognitive and normative institutional elements of context as the most relevant for local sustainability CSSPs, with regulatory elements not existing at the national level nor cultural-cognitive at the international scale. More importantly, results highlight trust, diversity, communication channels, sense of place, changing perceptions, and coopetition as key learnings to be considered for other partnerships in their design. Finally, with cultural-cognitive and normative elements speaking of the power of local features, it is these partnerships the ones influencing others beyond their scopes of action, with the potential of leading sustainability even further. However, associated activities and resources to provide stability and meaning to sustainability partnerships must be satisfied for that to happen.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".