Impact of Eel Training Programmes on the Farming Community - A Follow up Study in Karnataka
Bibliographic record
Abstract
Information, communication and population explosion along with science and technology in agriculture and allied sectors ale influencing the changes in the ideologies and objectives of the society. While the policies and programmes are formulated to generate and utilize the new resources, training of the people and personnel is seen as an integral part of development effort and also as means and ends of social change. Focusing on the faster growth of development in agriculture and allied sectors, a large number of training institutions have been established across the length and breadth of the country to achieve self-sufficiency in their respective sectors with the training effort at all levels. Still we need to meet the requirements of our larger population living below poverty line with continuous and well managed training programmes. Since training is a very costly affair involving number of inter connecting activities, it is highly essential for the training institutes and sponsoring organizations to know the effects of training programmes for the end users. With this in view, a two member team of EEl faculty (the authors) have taken up a follow-up study on training programmes conducted by Extension Education Institute, Rajendranagar. Hyderabad, Andhra Pradesh in three districts of Karnataka state namely Belgaurn, Bagalkote and Bijapur with an objective of assessing the extent of applicability of training programmes at field level and documenting the success cases. The overall applicability of the training programmes conducted by EEl was assessed from 58 trainees administering a structured questionnaire. Group wise discussions and presentations were also organized to bring out a few success cases that brought significant changes at field level. It was overwhelming to note that a majority of trainees (67%) expressed EEl trainings were useful to a greater extent at field level because of need based, practically applicable and well-designed content of trainings. The follow up team also interacted with the farmers and observed changes that occurred due to various interventions and documented the success cases.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".