“I like to take everything and put it in my own words”: Historical Consciousness, Historical Thinking, and Learning with Community History Museums
Bibliographic record
Abstract
This article presents ndings from a recent case study involving seventh-grade students ( n = 25) and a group of community history museum adult volunteers ( n = 5). Over 14 weeks, participants engaged in a series of scaffolding activities designed around a Material History Framework for Historical Thinking. The purpose of the inquiry was to explore pragmatic applications for historical thinking within a community history museum. Data collection included pre- and post- Canadians and Their Pasts surveys, written assignments, photovoice photography, in-depth interviews, and a nal class - room museum project. Conclusions are discussed within the context of Rusen’s (1987, 1993, 2004) typology of historical consciousness. This article presents a “call to action” for community history museums in Canada. It points to ways in which students can be empowered to become active members of a museum’s community of inquiry.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.031 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".