Measurement frameworks for assessing gender-related outcomes of agricultural extension interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it