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Record W4412115863 · doi:10.1080/23337486.2025.2530798

Feeling ‘like part of the action’: Scrutinizing TripAdvisor reviews of Western Canadian military museums

2025· article· en· W4412115863 on OpenAlexafffundabout
Matthew Ferguson, Kevin Walby

Bibliographic record

VenueCritical Military Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of Winnipeg
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeelingAction (physics)Media studiesPolitical sciencePsychologySociologySocial psychology

Abstract

fetched live from OpenAlex

Contributing to literature on war and military museums, this article analyses 1,378 TripAdvisor reviews of 34 military museums located in Western Canada. With new technologies and online experiences now shaping the commemoration of war and other violent events, we conceptualize TripAdvisor as a popular online space of contestation where visitor meaning-making occurs. We examine three themes present in the reviews: i) commemorating family military legacies and service; ii) the allure of intimate, realistic, and interactive content; and iii) elevating experiences through veteran staff and celebratory approaches. While war museums have been criticized for ‘pseudo-realism’ or deceiving tourists into thinking they have received an authentic experience with war, our findings indicate that this is a feature visitors seek out and contribute to (rather than challenge) through review-writing. In the conclusion, we reflect on the implications of these findings for the relevant literature and discuss strategies to encourage visitors to reflect on how ‘pseudo-realism’ can normalize and trivialize violence.

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)0.018
metaresearch head score (Gemma)0.072
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: none
Teacher disagreement score0.439
Threshold uncertainty score0.884

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.072
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0210.029
Science and technology studies0.0070.006
Scholarly communication0.0080.003
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.145
GPT teacher head0.340
Teacher spread0.195 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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