The Lives and Afterlives of Material Culture: New First World War Artifacts at the Canadian War Museum
Bibliographic record
Abstract
This article presents a selection of First World War artifacts that have been acquired by the Canadian War Museum since its opening in 2005. Each object is infused with multiple stories. Some were treasured mementos handed down through families, while others were nearly forgotten over time. Once at the museum, they acquired new narratives as these objects, artifacts and material culture are integrated into exhibitions, educational and digital products or accessed by researchers. The artifacts tell stories, contribute to our understanding of the diversity of Canadian experiences during the war and demonstrate the central role of the artifact in the museum.\nCet article présente une sélection d’artefacts de la Première Guerre mondiale qui ont été acquis par le Musée canadien de la guerre depuis son ouverture en 2005. Ces objets évoquent des histoires diverses, les uns, souvenirs précieux transmis par les familles, les autres, presque oubliés au fil du temps. Une fois acquis par le musée, les objets, les artefacts et la culture matérielle entament une nouvelle vie en s’insérant dans les expositions, en servant de matériel éducatif et numérique ou en étant mis à la disposition des chercheurs. Les artefacts racontent des histoires, contribuent à notre compréhension de la diversité des expériences canadiennes pendant la guerre et démontrent le rôle central de l’artefact dans le musée.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".