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Record W2289347849 · doi:10.1177/1206331215623220

Imperial Nostalgia, Social Ghosts, and Canada’s National War Memorial

2016· article· en· W2289347849 on OpenAlexaffabout
Tonya Davidson

Bibliographic record

VenueSpace and Culture · 2016
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAllegianceWorld War IISpanish Civil WarNational historyHistoryWhite (mutation)Element (criminal law)First world warLawSociologyAncient historyPolitical sciencePoliticsArchaeology

Abstract

fetched live from OpenAlex

The National War Memorial in Ottawa, Canada, occupies a central ceremonial square in the capital city. Placed in 1939 to commemorate Canada’s involvement in World War I, the monument is the featured element of the national Remembrance Day services, and it is often visited by dignitaries and bestowed wreaths. In this article, I suggest that beyond offering mere instruction in the history of Canada’s World War I involvement, the National War Memorial produces potent lessons in how to feel about being Canadian, and how to mourn as a Canadian. In particular, the National War Memorial has become a conduit for articulations of imperial nostalgia; it compels persuasive, ritual allegiance to Canada’s imperial White settler past. These affective relationships are produced with such success that other identifications—or the proposal of alternative meanings of the monument—are understood as acts of deviance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0290.043
Scholarly communication0.0080.002
Open science0.0010.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.269
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations16
Published2016
Admission routes2
Has abstractyes

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