Exhibiting War: The Great War, Museums, and Memory in Britain, Canada, and Australia
Bibliographic record
Abstract
What does it mean to display war? Examining a range of different exhibitions in Britain, Canada and Australia, Jennifer Wellington reveals complex imperial dynamics in the ways these countries developed diverging understandings of the First World War, despite their cultural, political and institutional similarities. While in Britain a popular narrative developed of the conflict as a tragic rupture with the past, Australia and Canada came to see it as engendering national birth through violence. Narratives of the war's meaning were deliberately constructed by individuals and groups pursuing specific agendas: to win the war and immortalise it at the same time. Drawing on a range of documentary and visual material, this book analyses how narratives of mass violence changed over time. Emphasising the contingent development of national and imperial war museums, it illuminates the way they acted as spaces in which official, academic and popular representations of this violent past intersect.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".