Museum Governance: Lessons From the Past as a First Step Towards Innovation
Bibliographic record
Abstract
Over the past thirty years, private sector governance has seen significant development in regulatory guidelines, best practices, and adaptation to emerging challenges. In contrast, the history of Canadian museum governance is relatively new. Not-for-profit Canadian museums began to establish governance frameworks only recently with influential publications like the UNESCO Recommendation on Museums and Collections (2015) and the SMQ Guide (2014), along with the implementation of Bill 114 in 2016. Bill 114 introduced constraints and responsibilities akin to those in the private sector, shifting governance from a more informal approach to a structured system. Notable examples of pre-Bill 114 governance include Rolland Arpin's initiatives at the Museum of Civilization and guidelines by the Canadian Art Museum Directors Organization. This study aims to explore the lessons that museum governance can learn from private sector practices, comparing regulatory frameworks and drawing insights from corporate governance literature to enhance the sustainability and effectiveness of not-for-profit museums and heritage preservation efforts.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".