Bibliographic record
Abstract
This chapter explores how heritage institutions, particularly museums, contribute to practices of democracy as spaces and media of knowledge-building used by “new” Canadians. What is represented in a museum, a public space, can affect how Canadian society sees itself, how outsiders see us, and who is defined as belonging to this community as citizens. Museums have historically been situated at the intersection of representation and citizenship, as both formally and informally inscribed. They represent and authenticate official statements about meaning and belonging, while at the same time serving as “neutral” public spaces for knowledge-building and citizen participation. Museums legitimize versions of a state or community’s history, what is accepted as heritage, who belongs to that heritage, who has membership and status within a community, and who does not belong. And expressions of nondominant players may be included or appropriated by this institution. Yet at the same time they serve as informal public spaces or arenas for social interaction and dialogue. The balancing of these seemingly incommensurate roles has been a central question in museology—representing and shaping citizens on one hand, and on the other serving as site and tool for alternative meaning-making, expressions, and participation in culture, heritage, and citizenship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".