Images of War in the North Atlantic Triangle
Bibliographic record
Abstract
This volume addresses representations of war and peace in various media, ranging from literature to film, photography, the visual arts, and music. Underlining the importance of an inter-disciplinary study of images of war and peace, the essays collected in this volume analyse how the choice of medium has contributed to shaping images of war in different historical contexts. Topographically, the focus is on wars in Europe, the United States, and Canada, yet the essays also address the global dimensions of warfare. A first section discusses general considerations regarding the representation of war, including socially and culturally engendered concepts of war, ideological attitudes and issues of language. The following sections concentrate on the American Civil War, with a comparative outlook on the 1930s Civil War in Austria, on representations of war by Canadian artists, and on the two World Wars. Here, a section on the First World War emphasises the iconographic significance of that war as the first industrialized inter-state war in history. Other essays deal with the inter-war period, and with the Cold War and after, when asymmetrical forms of war like the “war on terror” or the militant suppression of groups of the population began to blur conventional boundaries between war and peace.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".