Measurement frameworks for assessing gender-related outcomes of agricultural extension interventions
Bibliographic record
Abstract
Most agricultural extension evaluations that claim to measure gender outcomes rely on a single indicator typically female participation counts while overlooking gender equity's multidimensional nature. This research developed and tested a comprehensive measurement framework assessing gender-related outcomes across five dimensions: resource access, participation quality, decision-making authority, economic outcomes, and empowerment. A systematic review of 93 extension evaluations (2012-2022) identified measurement gaps informing the framework design. The framework was pilot-tested through longitudinal assessment of 14 extension programs across Ontario and Manitoba, Canada, tracking 342 women over 18 months (January 2021-June 2022) at the University of Guelph. Scores improved significantly from baseline to endline across all dimensions (p<0.001), with resource access showing the largest gain (31.4 to 62.3, d=2.14). Decision-making authority improved least (22.1 to 49.4) and showed the steepest post-program decline. The framework demonstrated strong reliability (?=0.91) and convergent validity with the WEAI (r=0.74). Single-indicator approaches captured only 38.4% of variance explained by the full framework.
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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 | 0.266 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".