Web-Based Accountability Practices in Non-profit Organizations: The Case of National Museums
Bibliographic record
Abstract
Abstract Stakeholder theory posits that accountability systems depend on the strength and the number of their stakeholders. This paper aims to analyze the empirical validity of stakeholder theory, focusing on accountability systems in the museum sector. Based on Wikipedia resources, we have selected all of the “National Museums” (134 museums) in the major developed countries: Australia, Canada, France, Germany, Italy, the United Kingdom, and the US. After we control for type of activity (art or other), cost per visitor and country, the results of an OLS multivariate model show that size of the museum, which is assumed to represent the strength and number of stakeholders, and the amount of funds received, which represents the power of a particularly salient category of stakeholders (donors), are the two main determinants of the accountability level. We conclude that accountability, in the absence of shareholders, is driven by the number and the power of different stakeholders, validating the stakeholder theory.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".