Clio’s Narrator – Training Volunteer Guides in a Canadian Social History Museum in 2023
Bibliographic record
Abstract
This thesis assesses the process and personal experience of volunteer guide training at Montreal’s McCord Stewart Museum. The McCord’s mandate has changed from honouring David Ross McCord’s objective of establishing “an Indian museum” to endeavoring to be “an agent of change for a more just society.” This is indicative of an activist trend among social history museums which are moving towards a more social-justice oriented approach, decolonising, empowering people, and supporting participatory democracy. In this context, the role of museum guides is changing from a monologic, lecture-based one to a dialogic approach in which guides are expected to interact with visitors who often come with their own perceptions. My study documents how the McCord's changes have resulted in substantial revisions to its volunteer guide training program. This has evolved from script-based teaching emphasizing a museum's collection to the methodologies of 'new museology' that promote skills in participation, learning, and tolerance. My preliminary data reflects the changing expectations of guides in social history museums more generally, with a particular focus on how guides can create emotional engagement for museum visitors. My discussion draws on my field notes as I trained to be a volunteer guide and interviews with individuals involved with the McCord’s guide training program. While most volunteer guides resigned when the McCord closed during the Covid pandemic, which began in March 2020, my interviews indicate that several factors led to their leaving, including changes in the McCord's approach to training while recasting itself as an 'activist museum'.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".