Between Professionalism and Amateurism in the Use of the Agricultural Training Videos: Lessons Learnt from Experimental Auctions in Benin
Bibliographic record
Abstract
Agriculture training videos (ATV) facilitate the wider dissemination of agricultural technologies and encourage farmers to adapt the learnings to their conditions. However, producing quality video is expensive as well as challenging for notable number of agricultural extension organizations across many Africa countries. This paper compares farmers' willingness to pay (WTP) for two distinct video quality levels—a cheap, amateurish video and a costly, professionally produced video. Using stratified random sampling, 91 farmers were selected for the experimental auction sessions. These farmers were chosen from a database that was compiled by the Ministry of Agriculture developed in 2021, with a total population of 382 vegetable farmers. Data were analyzed using Student's t-test and Tobit model. 97% of farmers who attend the experimental auction sessions, agreed to pay for the ATV and results show a significant difference of 0.063 USD between the two videos, and in favor of professional video. Farmers’ WTP for ATV is influenced by education, access to funding, images clarity, type of character in videos, understanding level of message and language spoken. Video quality is very important in the learning process in rural areas and support the promotion of professional videos. However, amateur videos can be used in agricultural training if financial support are not available for professional videos production.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".