Analysis of Factors Influencing Discontinuance of Technology Adoption: The Situation with Some Nigerian Farmers
Bibliographic record
Abstract
<p class="StandardTextkrperSAR">The study identified the factors influencing the discontinuance of improved rice technologies in Nasarawa State of Central Nigeria. Multi-stage random sampling was purposely used to select eighty rice farmers from four rice-producing villages of the study area using structured interview schedule on the respondents. Statistical analysis involving frequency counts, means and percentage were used to satisfy objectives 1, 2, 3, and 4 while regression analysis was applied to satisfy objective 5. The results of the regression analysis showed that education and extension contact had significant but negative relationship at 5% level; while age had positive and significant relationship at 1% level with discontinuance of adoption of improved rice technologies. Farmers should be encouraged to participate in the on-going government rural literacy campaign while extension contact be enhanced to minimize discontinuance of improved rice technologies.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.010 |
| 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) | 0.000 | 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 teacher head, 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".