Understanding control in nonprofit organisations: moving governance research forward?
Bibliographic record
Abstract
Purpose – The purpose of this paper is to introduce the concept of organisational control and both its importance and utility for understanding nonprofit organisations. Design/methodology/approach – This paper uses a critical realist (CR) methodology to discuss the concept of control and its utility to research on governance of nonprofit organisations. Findings – The current study offers a conceptual framework that presents a holistic view of control, relevant for analysing nonprofit organisations, and a methodological lens (CR) through which this framework can be implemented. Research limitations/implications – This paper suggests that studies of governance should consider different levels of analysis, as suggested by examining the concept of control using a CR framework. This notion has yet to be tested empirically and a framework for examining governance from a CR perspective of control is suggested. Context is highly relevant to understanding control, and thus, this model requires testing in a wide diversity of nonprofit sectors, sizes of organisations and time periods. Originality/value – The literature on organisational control provides useful insights to advance our understanding of nonprofit organisations beyond the notion of governance, and this paper proposes both conceptual and methodological underpinnings to facilitate future research.
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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 | 0.034 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.056 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".