Bibliographic record
Abstract
Abstract : How do you bring the museum to the classroom? In 2014, the Canadian War Museum introduced the Supply Line program to mark the centenary of the First World War, circulating Discovery Boxes of real and reproduced objects from the Great War to schools across the country, reaching Canadian students in every province and territory. With the arrival of the 75th anniversary of the end of the Second World War, Museum staff, in consultation with teachers, added new Second World War Discovery Boxes that are proving as successful as the first program. This article discusses the development process behind the Second World War Supply Line kit.\nComment transporter le musée dans la salle de classe? En 2014, le Musée canadien de la guerre a lancé la programme Ligne de ravitaillement pour marquer le centenaire de la Première Guerre mondiale, en faisant circuler des Boîtes de découverte qui contiennent des artefacts authentiques et des reproductions liés à la guerre dans les écoles du pays. Elèves dans chaque province et territoire du Canada ont profité du programme. À l’occasion du 75e anniversaire de la fin de la Seconde Guerre mondiale, le personnel du musée, en consultation avec des enseignants, a ajouté de nouvelles boîtes de découverte avec artefacts de la Seconde Guerre mondiale, et qui ont autant du succès que celles du premier programme. Cet article traite du processus derrière la mise au point de la trousse de Ligne de ravitaillement de la Seconde Guerre mondiale.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.139 | 0.023 |
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".