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Record W6992444812

The Lives and Afterlives of Material Culture: New First World War Artifacts at the Canadian War Museum

2022· article· en· W6992444812 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsFirst world warWorld War IIObject (grammar)NarrativeArtifact (error)Spanish Civil War
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.209
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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