Decomposing the Impact of Leadership Diversity Among Nonprofit Organizations
Bibliographic record
Abstract
Our contribution lies in exploring loci and reach of leadership diversity’s influence on proximal and distal performance outcomes to understand how and where these can be mobilized. Our moderated-mediation modeling decomposes the direct, indirect, and interaction effects of demographic diversity among three types of focal actors in governance—Boards (gender-, age-, and ethno-racial variety), Board Chairs (gender and ethno-racial demography), Chief Executives (gender and ethno-racial demography)—on five factors reflecting functional and social dimensions of Board Performance and two dimensions of Organizational Performance. We demonstrate that the Board composition affects proximal board performance outcomes, whereas CEO demography is more related to distal organizational performance outcomes. Board Chairs, a less-examined aspect of nonprofit governing, stand out as bridging both proximal and distal outcomes, both directly and through their interactions with Board diversity and CEO demography.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".