Assessment Of Entrepreneurship Development Through Attracting And Retaining Youth In Agriculture (ARYA)
Bibliographic record
Abstract
Declining rural economy and increasing migration of rural youth are affecting the growth of India’s economy. To encourage rural youths in agriculture Indian Council of Agricultural Research (ICAR) had initiated a programme on Attracting and Retaining Youth in Agriculture (ARYA) through selected Krishi Vigyan Kendras of the country. The programme was also carried out through nine KVKs on Zone V located in Odisha and West Bengal. It has been observed in a study that rural youths are eager towards entrepreneurship development but lack of technical expertise and financial constraints are preventing them. Under ARYA potential enterprises of the districts were identified and on and off campus trainings were provided. After continuing the project for three years (2019-20 to 2021-22) through nine KVKs, an assessment on income improvement, employment generation and socio-economic status was carried out with a sample size of 240 ARYA beneficiaries. The result revealed that 42 enterprising units, 29 groups and 223 enterprises were established. 272 nos. of employment were also generated through 130 successfully running enterprises. Among the enterprises poultry provided highest average income followed by mushroom, fishery, apiary, horticulture nursery, goatery, vermicompost and scientific lac cultivation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".