Accountability In The Stakeholder-AgencyTheory: Evidences From Museum Sector
Bibliographic record
Abstract
Stakeholder theory posits that accountability systems \ndepend on the strength and the number of \ntheir stakeholders. This assumption is more verifiable \nin non-profit organisations in the absence of dominant \nshareholders. This paper aims to analyse the \nfunctioning and the empirical validity of stakeholder \ntheory, focusing on accountability systems in the museum \nsector. As organisations that the public entrusts \nwith the responsibility to care for our shared artistic \nheritage, museums must continually re-assess and \nre-affirm their commitment through accountability. \nBased on Wikipedia resources, we have selected all \nof the “National Museums” (134 units) in the main \ndeveloped countries: Australia, Canada, France, Germany, \nItaly, the United Kingdom, and the USA. After \nwe control for kind of activity (art or other), cost per \nvisitor, and country, the results of an OLS multivariate \nmodel show that size, which is assumed to represent \nthe strength and number of stakeholders, and the \namount of funds received, which represents the substantial \npower of donors, are two determinants of the \naccountability level. We can thus conclude that accountability, \nin the absence of shareholders, is driven \nby the number and the power of different stakeholders, \nvalidating the stakeholder-agency 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 | 0.021 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".