Traditional Knowledge of Marine Resources and Their Relations to the Coastal Community’s Economic Well-Being in Peninsular Malaysia
Bibliographic record
Abstract
This study was conducted to analyse the practice of associated use of traditional knowledge (TK) on marine resources as new sources of wealth to enhance the economic well-being of the coastal communities. This study employed a qualitative research approach with a total of 117 respondents in Peninsular Malaysia. Convenience and purposive sampling methods were used to identify potential respondents and thematic analysis for the data analysis. The data was gathered through telephone interviews during the Movement Control Order (MCO) in Malaysia due to the COVID-19 pandemic. The results of the study reveal that the associated use of traditional knowledge on marine resources does play an essential role in the economic well-being of coastal communities. This was assessed through the perspective of living standards and livelihood improvement. The potential of TK based on marine resources illustrates the opportunity for this practice to become a valuable economic resource and create employment opportunities among coastal communities to generate a sustainable source of income. This study can also change modern society's perception of the importance and benefits of TK based on marine resources. The implications of this study can be viewed from the theory, practice, and policy. Future studies could be expanded to the Borneo region of Malaysia to gather different perspectives, and a quantitative approach could be used to gather more extensive opinions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".