Dependency, Exploitation and Poverty among the Labourers of the Fishing Community
Bibliographic record
Abstract
The fishing community is mainly dependent on the harvest of fisheries resources to meet their social and economic needs. More than two million people in Sri Lanka are directly or indirectly dependent on the exploitation of fisheries resources. The wage labourers who are engaged in fishing are severely impacted by poverty even though they make a significant contribution to the economy of the country. With regard to this, this study focused on how dependency and exploitation have shaped the life of the poor wage labourers in the fishing community, based on Andre Gunder Frank’s Dependency theory. Most of the labourers in the fishing community suffer a poor living standard. They are badly exploited by some other actors operating in their working environment. The investors (Mudhalalis) and intermediaries are the people who exploit the labour of the poor fishermen and turn them into dependent people through loans provided by them. Poverty, inadequate housing, poor health, illness and treatment, education of children, inadequate infrastructural facilities, and family problems including domestic violence were found to be the causes of dependency and exploitation of the fishing community. These issues need to be addressed to enhance standard of living of the fishing community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".