Constructing A Local Network for Effective Implementation of Integrated Coastal Management
Bibliographic record
Abstract
Integrated Coastal Management(ICM) has been recommended by international organizations and experts as a desirable way of dealing with the current problems of ocean pollution and ocean conservation and dealing with the conflicts among the various users of coastal and ocean resources. As a response, the Korean government legislated Coastal Management Act in 1999. following the Act, local governments were required to make a local integrated coastal management plan(LICMP). Though the LICMPs are made, it is not easy to put LICMP in effect, because the mandates of the Coastal Management Act are not clear and there are conflicts regarding the jurisdiction of the coastal areas among relevant departments of the government and because it is not easy to monitor and supervise the activities along the vast areas of coasts and oceans. The traditional method of Implementing the LICMP was not simply feasible. Community-based approach to the ICM was proposed as an alternative to the traditional method. This study aims to examine and introduce the community-based network of organizations as an alternative form of organization best suited to the integrated coastal management. This study is composed of four major parts. First, it examines the advantages of the network as a form of organization vis-vis the market and the hierarchy. Second, it reviews three well-known cases of integrated coastal management programs - Xiamen ICM program in China, Coastcare in Australia and Atlantic Coastal Action Program in Canada. Third, on the basis of the case study, it proposes principles and guidelines which we need to consider when we introduce the community-based approach to the ICM in Korea. Fourth, this study also reports on the actual networking processes in Yeosu City(the Yeosu Network for the effective implementation of integrated coastal management plan). The networking in Yeosu will serve as a demonstration of networking various stake-holders concerned with the balance between the development and conservation of finite ocean resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".