Traditional Knowledge of Marine Resources and Its Impact on the Well-Being of Coastal Communities in Peninsular Malaysia
Bibliographic record
Abstract
Traditional knowledge (TK) concerning the utilization of marine resources has long been employed by coastal communities for their general well-being and income generation.This study aimed to explore the potential of TK related to marine resources for enhancing the well-being of coastal populations in Peninsular Malaysia.A qualitative approach was adopted, with in-depth interviews conducted among 117 participants in the region.Data collection took place during the Movement Control Order (MCO) imposed in Malaysia due to the COVID-19 pandemic, which restricted the ability to conduct face-to-face interviews.The findings of this investigation revealed that TK associated with marine resources plays a crucial role in promoting health among coastal inhabitants.This was assessed by examining the key resources and purposes for which TK was employed within these communities.The present study has the potential to contribute to the development of new knowledge on TK practices, provide valuable information to assist coastal populations in augmenting their income and health, and serve as a reference for governments, agencies, and relevant stakeholders in recognizing the significance of TK.Future research could extend to the Borneo region of Malaysia to obtain diverse perspectives, and a quantitative approach may be employed to achieve a broader generalizability of findings concerning the relationship between TK based on marine resources and societal well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".