The power‐structure model of non‐profit governance
Bibliographic record
Abstract
Abstract Research Question/Issue The Power‐Structure Model of Non‐Profit Governance provides an understanding of the types of power exhibited by different stakeholders in nonprofit organizations. Research Findings/Insights This qualitative study involved 21 semistructured interviews with board chairs of socially focused nonprofits. A grounded theory‐based approach was used to code extracted data into grouped concepts and to develop the model. Findings from the study indicate that (1) the reward and legitimate power of funders trumps almost all the other types of power in a nonprofit organization, (2) a board's legitimate power over management cannot surmount management's informational power, and (3) there may be a power struggle between the board chair's informational/referent power and the board members' expert/referent power. Theoretical/Academic Implications The Power‐Structure Model of Non‐Profit Governance, which was conceived using theory and qualitative data, incorporates French and Raven's (1959) five types of social power to outline the power structure of nonprofits and how it differs from the traditional organizational structure. The model offers new perspectives on researching power and decision making in organizations and how appropriate governance can help to reduce power asymmetry between the CEO and the board. Practitioner/Policy Implications The Power‐Structure Model of Non‐Profit Governance offers insight for policy makers into the types of power available to different actors in organizations, how they use this power, and how this power structure plays out in various organizations.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".