Ethno-racial Diversity on Nonprofit Boards: A Critical Mass Perspective
Bibliographic record
Abstract
The need for greater diversity among organizational leaders and directors remains a challenge for organizations within the third sector, and beyond. This study examines diversity through a critical mass lens; that is, we examine an alternative approach to understanding the relationship between the ethno-racial composition of boards of directors and their perceived ability to engage stakeholders, improve organizational responsiveness, and effectively manage fiduciary responsibilities. Our study, drawing on a survey of 247 boards, clarifies the need for a critical mass approach to leadership diversity by highlighting the uneven impact of diversity on performance demonstrated by periods of accelerating and decelerating effect. We find that boards achieving a critical mass of ethno-racial diversity improved board performance among three governance activities—fiduciary performance, stakeholder engagement, and organizational responsiveness—with our critical mass approach illustrating the uneven impact of diversity on performance for each governance activity.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".