Governance in transition: exploring people’s mindset and institutional matches \ntowards a governable coastal fisheries in South Korea
Bibliographic record
Abstract
Many fisheries challenges are closely linked to the choice of governance style, with the typical top-down, hierarchical mode unable to effectively cope with ever more diverse, complex, dynamic, and multi-scalar fisheries reality. Subsequently, transition towards a co-management type has been a popular trend in many coastal fisheries around the world. Although this initiative has shown potential in bringing positive outcomes to local fishery and communities, in many cases the transition process has proved to be a ‘wicked’ undertaking with multiple intricate issues emerging to complicate the efforts and to frustrate community members, practitioners and researchers alike. Recognizing the need for alternate insights into these implementation challenges, this thesis argues for a thorough understanding of governance change by highlighting the importance of ‘meta-order governance’ elements, such as values, images and principles, of various fisheries stakeholders in shaping its outcomes. Further, it calls for an investigation of the institutional aspect of governance to underscore the structural elements being promoted in the transition and to elucidate its fit with the meta-level notions of governance actors, including the local fishers affected by them. These two areas of inquiry are inspired by the interactive governance theory and the governability concept, which emphasizes the need to examine all aspects of a governance system and their interconnectivity in order to solve problems and create societal opportunities. A government-initiated fisheries co-management program currently underway in South Korea, called ‘Jayul’, forms the context in which this new focus is applied. The main research question this thesis aims to explore is “how does the governance change instituted by the central government align with what fishers fundamentally conceive to be important and desirable for the fishery?” In addition to theoretical conceptualization, the research has a strong emphasis on method development, given the knowledge gap in the elicitation of values, images and principles in empirical settings. The approach advanced here can be extended to examine the implementation of other fisheries governance initiatives, such as marine protected areas, individual transferable quotas and seafood certification schemes, to provide a useful way of understanding their standings and prospects. In the process, new insights may surface, challenging and improving the core ideas raised in this research.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".