Merging Developmental and Feminist Evaluation to Monitor and Evaluate Transformative Social Change
Bibliographic record
Abstract
Programs seeking to challenge and change gender and power relationships require a nimble, evolving monitoring, evaluation, and learning (MEL) system that helps make sense of how nonlinear complex social change happens. This article describes efforts by Oxfam Canada to develop such a system for a women’s rights and gender equality program. The system, which we call a feminist learning system (FLS), is an interconnected, nonlinear system that emerged over the program life cycle and responded to evaluative challenges and information needs we encountered along the way. The learning-oriented focus of the system differentiates it from more standard approaches to monitoring and evaluation. We situate the system within current evaluation thinking and research, arguing that it represents a merging of developmental evaluation and feminist evaluation. The synergistic fit of the two approaches provided an evaluative framework that strengthened Oxfam Canada’s ability to monitor, evaluate, and learn from our highly complex program. It also provided a lens that viewed MEL activities as part of a continuum of social transformation that reinforced programmatic goals related to women’s rights and gender equality.
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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 | 0.139 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".