“Dear Brother Farmer”: Gender-Responsive Digital Extension in Tunisia during the COVID-19 Pandemic
Bibliographic record
Abstract
Providing farmers with essential agricultural information and training in the era of COVID-19 has been a challenge that has prompted a renewed interest in digital extension services. There is a distinct gender gap, however, between men’s and women’s access to, use of, and ability to benefit from information and communication technologies (ICTs). The overall purpose of this research is to examine how digital extension can address gender inequality in rural areas in the context of the COVID-19 crisis by designing and evaluating the gendered impacts of a digital extension intervention delivered to 624 farmers (363 men and 261 women) (which included phone distribution, radio and SMS messages, and sharing of information prompts) in northern Tunisia. In order to assess the effectiveness of gender-responsive digital extension that targets husband and wife pairs, as opposed to only men, we employed logistic regression and descriptive statistics to analyze a sample of 242 farmers (141 women and 141 men). We find that phone ownership facilitated women’s access to their social network, as well as agricultural information and services, ultimately improving their participation in household decision making and agricultural production. We find that gender-responsive digital extension is effective for men and especially women in terms of usefulness, learning, and adoption. We identified education level and cooperative membership as important factors that determine the impact of digital extension services on farmers and demonstrate the positive impact of radio programming. We recommend strengthening phone access for women, targeting information (including through non-written ways) to both husbands and wives, using sharing prompts, and more rigorous extension for knowledge-intensive topics such as conservation agriculture and rural collectives.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".