The public extension in the last quarter century in Manisa Province in Türkiye
Bibliographic record
Abstract
Objective: The objective of this study was to reveal some extension indicators in the province, to contribute to the extension memory of the country, and to develop advice for the extension system by examining the change in the last quarter century. Material and Methods: The main material of the study consisted of the data from 229 extensionists working in Manisa Provincial Directorate of Agriculture and Forestry and some district directorates. Logistic regression analysis was used to determine factors affecting occupational satisfaction of extensionists. Results: The number of young, female and university educated extensionists has increased. While the time devoted to extension has decreased, the bureaucratic workload has increased. The number of farmers served by an extensionist increased by 43% during the covered period. Extensionists who spend a lot of time on farmer training were found to become satisfied with their work. Conclusion: The employment policy should be planned in such a way that one extensionist serves 200 farmers. Extension activities should be carried out with project logic by defining criteria such as time, region, targets, budget, opportunities, collaborations, and result indicators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".