Improving the performance of multi-stakeholder partnerships for sustainable development in coastal areas : Sweden (Hanö Bay) as a case study
Bibliographic record
Abstract
Coastal areas are vital for both ecosystems and human societies. Comprising diverse terrestrial, freshwater, and marine ecosystems, coastal areas provide us with essential resources and services. However, these areas are under threat from human activities and climate change, necessitating new governance structures to ensure their sustainable management and conservation. This research investigates how to improve the performance of local multi-stakeholder partnerships (M-SPs) in coastal areas, promoted as key mechanisms for achieving sustainable development goals. By drawing on stakeholder theory and using Pattberg & Widerberg’s (2014) analytical framework with nine building blocks for successful M-SPs as a foundation, the study examined drivers, challenges, and success factors of local M-SPs, as well as how these factors relate to one another. Additionally, a qualitative case study approach was adopted, focusing on a coastal M-SP situated in the south-east coast of Sweden, specifically Hanö Bay. Furthermore, mixed methods were employed to collect and triangulate the data necessary to delve deeper into the nine success factors identified within the analytical framework as applied to the case study. These methods encompassed a scoping review, focus group discussions, questionnaires, and participant observations. To validate and frame findings, a reference case in Canada, the Atlantic Coastal Action Program (ACAP), was selected for focus group discussions, leveraging the existing collaboration between the two coastal regions. The research identifies the critical role of relational aspects in improving the performance of coastal M-SPs. The current analytical framework, while acknowledging the importance of partner selection and power dynamics, overlooks interpersonal dynamics and conflicts. Empirical findings, supported by stakeholder theory and recent research, emphasize the necessity of addressing these relational challenges to foster collaboration. Thus, incorporating “relational aspects” as a tenth distinct factor in the framework is recommended. Secondly, the findings illuminate the intricate and interconnected nature of all factors in the analytical framework influencing the performance of coastal M-SPs, further emphasizing the necessity of adopting a holistic approach and addressing all ten factors in concert to enhance the efficacy of such partnerships. The study makes theoretical contributions to the analytical framework used and to stakeholder theory as a whole, as well as practical and policy contributions to the use of M-SPs as an implementation mechanism for sustainable development of coastal areas.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".