Participatory Evaluation of ‘<i>Irorunde</i>’, A Prototype Drum Oven for Traditional Fish Smoking in Nigeria: A Case Study of Knowledge Co-Production and Inclusive Innovation
Bibliographic record
Abstract
In Nigeria, traditional fish smoking methods predominantly utilize firewood as an energy source, which presents sustainability challenges. Improved fish smoking techniques face low popularity owing to considerable obstacles hindering adoption by fishers involved in fish smoking and development by inventors. This study explored the benefits, challenges and social acceptance of a modern drum oven prototype by fisheres engaged in fish smoking. Against this backdrop, Participatory Action Research (PAR) was conducted using a prototype kiln using carbonized biomass briquettes (CBB) in traditional fish smoking drum ovens. Fishers involved in fish smoking performed evaluations of the prototype, and their perceptions regarding the characteristics of innovation were utilized to assess their willingness to adopt the prototype. The WhatsApp platform was used to share information and promote peer-to-peer learning. The PAR and evaluations by the fishers led to improvements in the design, construction and performance outputs of the prototype. The fishers agreed that CBB was economical and was a cleaner energy source, facilitating social acceptance and the adoption of the prototype as a substitute for the local drum. The portable size, quality and aesthetic structure also contributed to the adoption of the prototype. In conclusion, the prototype became a socio-economic tool that has encouraged the use of CBB in fish smoking with improved financial and health benefits of the fishers engaged in fish smoking. Local technologies must incorporate inclusive innovation and gender-responsive approaches to facilitate implementation and adoption, thereby improving benefits and well-being of the fishers.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".