Agricultural training for Pakistan’s rural women
Bibliographic record
Abstract
While women play an equally important role as men in Pakistan's farming systems, they typically have little access to information on modern farming techniques due to cultural rules that prevent them interacting with male agricultural extension staff.In 2013 and 2014, CABI initiated farmer training activities specifically targeting women in two areas of the country: Gilgit-Baltistan in the high north, and southern Punjab in the flatter central part of Pakistan.In the north, CABI worked with a local development project to set up farmer field schools for both men and women; these focused on production of tomatoes and dairy livestock.In southern Punjab, CABI's Skills for Farms project ran a series of three-month, village-based training courses in kitchen gardening and grain storage, targeting women between 16 and 35 years old.In each case, the training courses were designed to focus on household farming activities that are typically the responsibility of women.In Punjab, attitudes towards the training of women had become more positive following the floods of 2010, when numerous development organisations set up support programmes in the area to help communities restore their livelihoods.In Gilgit-Baltistan, there was much greater initial scepticism and criticism of women's involvement in the field schools.However, after the first batch of trainees was seen to make significant progress in their farming knowledge and practices, community attitudes to the programme were transformed.Women's influence and respect within their households also improved.Prior to the training, less than 5% of women interviewed in southern Punjab reported having a say in household spending decisions; that figure rose to almost 50% after the training.Beyond the household, trained women have also become more respected in their communities as a source of knowledge on modern agricultural practice.Importantly, impact research has also revealed different responses among women and men in adopting the new farming methods.In Gilgit-Baltistan, all women trainees were found to be earning more money from their tomato and dairy activities, which they had significantly increased following the training.Men, in contrast, were less interested in the production aspects of the training, which they tended to pass on to female household members, and were more interested in 'monetising' the training, through the marketing skills they were taught.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".