Effects of Mobile Phone Use on Artisanal Fishing Market Efficiency and Livelihoods in Ghana
Bibliographic record
Abstract
Abstract This article assesses the effects of mobile phone use on the artisanal fishing industry in the Effutu Municipality of Ghana. It contributes to the growing literature on how mobile telephony can help overcome market inefficiencies in developing countries due to imperfect information. The study shows how mobile phone use among fishermen has enhanced the efficiency of input and output markets for artisanal fishing and improved their businesses relations and livelihoods. The ‘before and after’ approach was used, based on interviews with fishermen and other supply chain actors on ways in which fishermen bought inputs and sold fish, and their perceptions of the effects of the mobile phone. The results indicate that market efficiencies improved and price variations reduced as a result of better availability of up‐to‐date information. Use of mobile phones enabled fishermen to improve their incomes, expand their markets, feel more secure at sea, and remain in closer touch with both families and other 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 | 0.001 | 0.003 |
| 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) | 0.008 | 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".