The Field-Building and Grantee Experimentation Role of Foundations in Impact Investing as Illustrated by a Gender-Lens Investing Case Example
Bibliographic record
Abstract
This article argues for foundations to play two critical roles in the impact investing ecosystem: to commission and/or support research that helps build more equitable and socially just impact investing and to fund grantee-specific experimentation in areas of impact investing and social enterprise that are nascent or developing. To illustrate what this can look like, this article presents action research conducted on gender-lens investing, describing in detail a 2019 Mastercard Foundation grant to Engineers Without Borders Canada. The project involved two main goals: testing and developing gender-lens investing tools and processes with seed-stage investees during pre- or post-investment phases and evaluating the implementation of Engineers Without Borders Canada’s gender-lens investing strategy and the assumptions underpinning it. Field-building products that resulted from the grant included a report on the lessons learned and a comprehensive literature review on gender-lens investing in sub-Saharan Africa that contributes to a growing evidence base. This article details the purpose, approach, results, and immediate impact of the action research and evaluation for Engineers Without Borders Canada for Mastercard Foundation and for the field. Further, the article highlights how the grant continues to impact Engineers Without Borders and the participating ventures today, and why it is important for foundations to play the role of field builder and make grants to support experimentation and field building, especially around issues of equity.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".