Understanding control in nonprofit organisations: moving governance research forward?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it