Evaluating Efforts to Shift Power Through Listening: Defining Power and Listening to All Sides
Bibliographic record
Abstract
There are a variety of ways funders and nonprofits are trying to shift the power dynamics between those who typically have held it (i.e., funders and organizations) to those who have unique and important expertise: those most affected by the problems the social sector is trying to solve. In this article, we share lessons from two evaluations that sought to understand how those who were being listened to experienced the listening process, exploring the degree to which people felt heard and their experiences of how, and in what ways, power shifted for them. One evaluation focused on the experiences of individuals who had given feedback to nonprofits serving them. While this evaluation focused on nonprofit organizations, we believe the lessons learned about how feedback and the act of listening itself influenced organizational change and advanced equity within organizations has relevance for funders, as well. In the second, we explicitly explored questions of power-shifting with participants in and grantees of a participatory grantmaking initiative. By looking across these two sets of evaluation findings, we elevate ways of thinking about power, as well as new considerations and implications for funders who seek to listen to the people most impacted by the problems they are working on.
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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 | 0.494 | 0.567 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".