Developing a Community of Practice (CoP) on Monitoring, Evaluation, and Learning (MEL) in a Global Network of Women’s Funds
Bibliographic record
Abstract
This paper provides a structured description and analysis of the development and implementation of a community of practice (CoP) framework for monitoring, evaluation, and learning (MEL) within women’s funds (WF) around the world, particularly members of the Prospera International Network of Women’s Funds (Prospera-INWF). The Prospera-INWF is the global hub of women’s funds that advocates for resource justice within philanthropy. It supports its 44 members in strengthening institutional capacity that is critical for women’s funds to be resilient and transform the world for women, girls, trans, intersex, and non-binary people and their communities. Co-authors are three members of the Prospera-INWF Secretariat Team, one representative of a multi-regional fund, and two technical consultants who were involved in the CoP development process. Considerations for how feminist principles strengthened the design and implementation of the CoP are discussed. Emergent key learnings of applying feminist principles to develop a CoP that focuses on feminist MEL practice are detailed, including CoPs as a critical, transformative methodology (and not merely a technical strategy). While learnings are gleaned through the specific Prospera-INWF CoP, they may provide useful insights for researchers and practitioners of MEL, adult learning, and evaluation capacity building (ECB), particularly those aiming to engage feminist principles in their work.
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.
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 | 0.210 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.024 | 0.039 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".