Establishment of Sustainable Management Model for the Ujungpangkah’s Mangrove Essential Ecosystem Area, Gresik – East Java
Bibliographic record
Abstract
The Ujungpangkah Mangrove Essential Ecosystem Area (MEEA) has significant ecological and strategic value in maintaining the stability of the coastal ecoregion.However, this area faces complex socio-ecological pressures from abrasion, accretion, land conversion, and expanding coastal economic activities.This study aims to analyze the range of community perceptions and preferences in managing the Ujungpangkah MEEA, develop a participatory management model, and design a recreational opportunity spectrum (ROS) for ecotourism programs.The research was conducted over four months (June-September 2025) using an exploratory mixed-method approach, combining secondary and primary data analysis.Results show a complex transition between ecosystem degradation and recovery; with 2015-2025 data showing an increase in vegetated areas from 1,624 ha to 2,248 ha (+38.4%), and an increase in built-up areas from 1,648 ha to 2,418 ha (+46.7%).Socially, data reveal no polarization in community perceptions of MEEA management, although differences in attitude scores exist between groups influenced by motives, experiences, and local regulations.The study contributes theoretically by deepening the cognitive framework for community participation in MEEA governance.Practical recommendations emphasize three strategic directions: strengthening socio-ecological restoration and ecotourism programs, optimizing adaptive and participatory governance mechanisms, and aligning multi-level policies with strengthened local regulatory instruments.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".