Bibliographic record
Abstract
Ontario has more than 500 museums, varying in type, size, and expressed need. They have different relationships to both government policy and the associations that represent them. Yet, research on museum governance often focuses on provincial or national organizations, neglecting community museums (i.e., smaller institutions with local or regional roots). Due to their limited resources, community museums rely on the work of spokespeople to advocate for their interests. Within Ontario museum governance, these spokespeople use the term “museum community” to indicate consensus on a course of action. According to a sociology of translation perspective, when a spokesperson speaks for others, they must first silence those in whose name they speak. As such, this paper considers how those governing the sector construct the “museum community” as actors in support of particular action. It asks who and what forms the museum community? Which voices are given a platform as museum advisors and which associations represent the so-called community? The paper concludes that municipal museums have historically had a privileged position within museum associations’ articulation of community, while provincial museum advisors have more successfully included the voices of small historical society museums. As the museum advisor’s resources have become more limited, the Ontario Museum Association (OMA) has taken a more active role in assembling those voices. However, the association has limited financial resources. As such, there continues to be a stratification of museums in museum governance.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".