Museums, Modernity and Conflict: Museums and Collections in and of War since the Nineteenth Century
Bibliographic record
Abstract
Museums, Modernity and Conflict examines the history of the relationship between museums, collections and war, revealing how museums have responded to and been shaped by war and conflicts of various sorts.Written by a mixture of museum professionals and academics and ranging across Europe, North America and the Middle East, this book examines the many ways in which museums were affected by major conflicts such as the World Wars, considers how and why they attempted to contribute to the war effort, analyses how wartime collecting shaped the nature of the objects held by a variety of museums, and demonstrates how museums of war and of the military came into existence during this period. Closely focused around conflicts which had the most wide-ranging impact on museums, this collection includes reflections on museums such as the Louvre, the Stedelijk in the Netherlands, the Canadian War Museum and the State Art Collections Dresden.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".