Improving the safety and quality of cotton production in Pakistan
Bibliographic record
Abstract
Since 2014, CABI has been working in partnership with the Better Cotton Initiative (BCI) in two districts of Sindh Province of Pakistan to train cotton farmers and farm workers in improved cultivation and harvesting practices.The aim is not only to improve cotton quality but also to promote practices that are safer in terms of both workers' health and environmental protection.Since the programme started, CABI has trained 11,360 farmers, 22,252 male farm workers and 16,000 female farm workers.In 2017 CABI commissioned a study to evaluate the effectiveness of this capacity building programme.It explored changes in behaviour and the financial, social and environmental benefits of the improved practices, as well as changes in awareness, opinions and attitudes resulting from the training.The study found that as a result of the training, cotton production practices including improved safety had improved.However the direct financial impact of improving cotton quality was disappointing.Although some ginners now pay a 1% premium for cotton produced according to BCI standards, this does not translate into higher pay for the farm workers.The main benefits for workers relate rather to improved health through safer practices, particularly reduced and safer pesticide use, which as well as improved quality of life translates into financial benefits through reduced healthcare costs and the ability to pick more cotton per day. Key highlights• As a result of the training, farmers are much more likely to use registered and properly labelled pesticides, to reduce the frequency of pesticide application, and to adopt a range of safety measure for themselves and their workers.However workers have to pay for safety equipment such as gloves and masks.• Women workers perceive that their own and their families' health has improved as a result of safer pesticide use, and that this enables them to pick larger volumes of cotton and so increase their income. Decent workDecent workplace conditions are a fundamental workers' right.The study explored farmers' awareness and understanding about decent work and wages in terms of five indicators: appropriate timing, good wages, no bullying, proper breaks, and toilet facilities.It found that the vast majority (85-100%) of BCI trained farmers are aware of each of these, compared to only 26-36% of untrained farmers.Furthermore, all BCI trained farmers believed that men and women should be treated equally and should get equal wages.Similarly, BCI-trained male workers were more aware of decent work issues, and of wage and gender equality, than untrained workers.
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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 | 0.001 | 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) | 0.003 | 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".