Accountability Theory in Nonprofit Research: Using Governance Theories to Categorize Dichotomies
Bibliographic record
Abstract
Abstract Nonprofit accountability research has garnered much attention in recent years, greatly expanding our understanding of the field. Yet, this focus has resulted in a complex and oftentimes fragmented body of research, which has made it difficult to navigate and effectively study nonprofit accountability. To address this concern, this article uses characteristics of accountability and articulates the dynamic interaction between the various accountability dichotomies found in the literature through a theory-based typology. In summary, this article argues that a narrow conception of accountability, focused on resource dependence, public interest and agency theories, can be seen as a functional process (“how”) to meet the imposed requirements (“for what”) of upward principals (“to whom”). In contrast, a broad conception of accountability, focused on stewardship, democratic, and stakeholder theories, can be seen as a strategic process (“how”) to provide information that is based on felt responsibility (“for what”) to downward stakeholders (“to whom”).
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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 | 0.006 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".