Traces of the Past: Raising the Allumettières (Matchworkers) in Sites of Collective Remembering
Bibliographic record
Abstract
Engaging with historical events, people and places encourages students to envision history as a dynamic process where individual, group, and national identities are reproduced. These types of educational interventions can foster recognition that history—both past events and our records about them—result from a process of authorship. The recent surge of interest amongst history educators and within recent publications of provincial curricula that focus on historical thinking concepts—historical significance, primary source evidence, continuity and change, cause and consequence, historical perspectives, and the ethical dimension of historical interpretations—encourages educators to consider ways to integrate these concepts within their teaching practice. Our case study of the narrative account of the allumettières (matchworkers) of Hull, Quebec is an example of one type of classroom inquiry into local places of remembering that could be taken up in the context of recent developments in, and aspirations for, the history curriculum. Our project invites readers to engage in the historical process of understanding the past within contemporary classrooms by drawing upon a range of interdisciplinary approaches including web-based exhibits and artefacts, visits to historic sites, published accounts and dramatic representations to meet these curriculum expectations.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.045 | 0.031 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".