Awareness, Training Needs and Constraints on Fishing Technologies among Small Scale Fishermen in Ondo State, Nigeria
Bibliographic record
Abstract
The role of fishing technologies in achieving the National goal of food sufficiency cannot be over emphasized. Many small scale fishermen who are supposed to be the end users of various modern fishing technologies are ignorant of various technological opportunities they stand to gain in their profession. Therefore, the objective of the study was to determine the awareness, training needs and constraints on fishing technologies among small scale fishermen in Ondo State, Nigeria. A multistage random sampling procedure was employed to select three local Government areas (Irele, Ilaje and Ese-odo), six fishing communities, and twelve artisanal fishermen to get a sample size of 216. Data were collected from the respondents using structured interview schedule and analyzed through the use of descriptive and inferential statistical tools. The results revealed that most of the artisanal fishermen were aware of the fishing technologies and 82.4% indicated favourable training needs towards fishing technologies. Major constraints experienced by the respondents were lack of proper net maintenance (76.9%), limited outboard engine repair workshop (73.6%) and effective fish processing, preservation techniques and equipment (70.4%). Significant relationship existed between awareness and training needs on fabrication of low cost fishing gears (X2 = 18.48; p < 0.00), smoking oven (X2 = 15.77; p < 0.00) and outboard engine repairs (X2 = 5.47; p < 0.01). Based on the findings of the study, concerted efforts should be made by all stakeholders to ensure that the required training needs of artisanal fishermen are met for the sustenance of fisheries technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".