The Information Practices of the Fishermen in the Bay of Bengal, Bangladesh
Bibliographic record
Abstract
Abstract Utilizing de Certeau’s concepts of “tactics” and “strategies,” and Chatman’s “information poverty,” this study examines the information practices of the fishermen in the Bay of Bengal, Bangladesh. Using faceto- face surveys, this study gathers data from 102 fishermen in the Bay of Bengal, Bangladesh. The findings of the study reveal that the majority of fishermen (“fisher folks”) studied regularly need information on weather, fish buying and selling prices, daily consumable products, entertainment, and religion. Fisher folks in this study heavily rely on their informal information networks (e.g., family and friends) to meet their diverse information needs. The study also reports various information challenges faced by the participants. It is evident in this study that fisher folks, due to unwelcoming information environment of strategic institutions (e.g., various government agencies), tactically avoid services and information provided by them. A call for radical change in “information service culture” (i.e., offering information to only educated, the dominant group of the society) has been emphasized by the author of the paper. The study also highlights the importance of offering appropriate, need-based, welcoming information services to rural communities by various government information agencies including public libraries. It is expected that this study will help researchers design studies aimed at exploring the “tactical information practices” of various unprivileged groups such as victims of domestic violence, ready-made garments worker, sex workers, etc., who have diverse socioeconomic and political backgrounds.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".