Empowering Communities in Sustainable Fishing Port Management: An Insight from Pondok Dadap Sendang Biru, Indonesia
Bibliographic record
Abstract
The fishing port of Pantai Pondok Dadap Sendang Biru, located in the coastal area of South Malang, East Java Province, Indonesia, serves as an essential hub for sustainable fish management and the primary livelihood source for the local community.This study identifies fish distribution patterns as a crucial aspect of effectively channeling products from fishermen to markets.We adopted a qualitative method for data analysis, utilizing the approach proposed by Miles and Huberman, which encompasses three main activities: data reduction, display, and conclusion drawing.Data reduction involves sorting through data to isolate the most relevant and significant information.The data were subsequently displayed in matrices, graphs, and categorical trees to facilitate a more straightforward analysis.Conclusions were drawn once the data were organized to highlight specific themes and patterns.For qualitative data, we used a Likert scale to gauge the attitudes, opinions, and perceptions of individuals or groups.Our findings reveal three primary distribution channels at the port: The first channel includes fishermen, distributors, and seafood processing companies; the second channel comprises fishermen, Kios Ikan Nelayan, and buyers; the third channel consists of fishermen, Kios Ikan Nelayan, and market sellers.Our results not only provide a framework for the sustainable management of the port but also offer an in-depth understanding of the fish distribution dynamics, which fishermen and port managers can utilize to enhance the productivity and sustainability of their fishing enterprises.Thus, this study contributes to the development of best practices in fisheries resource management and the empowerment of fishing communities in Indonesia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".