Livelihood Strategies of the Bajo Fishing Community in the Outbreak of COVID-19 (Study of Bajo People in Salabangka Island of Central, Sulawesi, Indonesia)
Bibliographic record
Abstract
This study aims to find out how Sama Bajo fishermen adapt to the seasonal moonson and environmental changes in the midst of the Corona Virus Desease (Covid-19) outbreak.The research conducted in one of the villages in the Salabangka Archipelago, precisely on Paku Island which is one of the largest islands in the Salabangka archipelago of Central Sulawesi Province, Indonesia.The study utilyzed the principle of a livelihood approaches.The adaptation strategies observed include; livelihood diversification, business intensification, utilization of social networks, asset sales and mortgages.The results showed that some of Sama Bajo fishermen carried out adaptation strategies, several livelihood adaptation strategies that were previously quite effective in overcoming the decline in income due to seasonal changes, currently could not be fully relied to tackle stress and shock.The development of several multinational mining investment activities on land has also resulted in pollution that affects the loss of seaweed cultivation which was previously become the mainstay of fishermen in times of famine.This situation has caused some Sama Bajo fishermen, especially the younger generation who have studied up to university to consider trying new livelihoods on land that were previously rarely done by Bajo fishermen.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".