Bibliographic record
Abstract
War Memories explores the patchwork formed by collective memory, public remembrance, private recollection, and the ways in which they form a complex composition of observations, initiatives, and experiences. Offering an international perspective on war commemoration, contributors consider the process of assembling historical facts and subjective experiences to show how these points of view diverge according to various social, cultural, political, and historical perspectives. Encompassing the representations of wars in the English-speaking world over the last hundred years, this collection presents an extensive, yet integrated, reflection on various types of commemoration and interpretations of events. Essays respond to common questions regarding war memory: how and why do we remember war? What does commemoration tell us about the actors in wars? How does commemoration reflect contemporary society’s culture of war? War Memories disseminates current knowledge on the performance, interpretation, and rewriting of facts and events during and after wars, while focusing on how patriotic fervour, resistance, conscientious objection, injury, trauma, and propaganda contribute to the shaping of individual and collective memory. Contributors include Joan Beaumont (Australian National University, Canberra), Gilles Chamerois (University of Brest, France), Subarno Chattarji (University of Delhi, India), Nicole Cloarec (Rennes 1 University, France), Corinne David-Ives (European University of Brittany - Rennes 2, France), Jeffrey Demsky (San Bernardino Valley College, California), Sam Edwards (Manchester Metropolitan University), Georges Fournier (Jean Moulin University, France), Annie Gagiano (University of Stellenbosch, South Africa), David Haigron (Rennes 2 University, France), Judith Keene (University of Sydney, Australia), Melissa King (San Bernardino Valley College, California), Christine Knauer (Eberhard Karls University Tübingen, Germany), Liliane Louvel (University of Poitiers), Michelle P. Moore (Canadian Army Doctrine and Training Centre, Kingston, Ontario), John Mullen (University of Rouen, France), Lorie-Anne Duech-Rainville (Caen University, France), Elizabeth Rechniewski (Australian Research Council Discovery Project), Raphaël Ricaud (University ‘Paris Ouest Nanterre La Défense’, France), Laura Robinson (Royal Military College of Canada), and Isabelle Roblin (Université du Littoral-Côte d’Opale, France).
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.159 | 0.049 |
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".