The role of board skepticism in strengthening nonprofit performance measurement and accountability
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
Abstract We interviewed 21 board chairs of nonprofits with social missions to ask how their organizations assess performance when they cannot adequately track the intangible work by employees or the outcomes of this work. We find that the uncertainty around tracking performance data makes boards skeptical of their ability to assess performance in these organizations. Our study suggests that the skepticism of these boards inspires effective strategies focused on decreasing uncertainty around performance. These strategies include tracking a combination of quantified data, process data, and narratives from employees and clients, and ensuring specific board processes that foster a psychologically safe environment for discussion and checking against cognitive biases. We find boards in these organizations to be aware, engaged, and effective at assessing performance and we suggest that policy makers can be better informed by accessing the knowledge and strategy used by these boards.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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