Bibliographic record
Abstract
As the centenary of the Great War approaches, citizens worldwide are reflecting on the history, trauma, and losses of a war-torn twentieth century. It is in remembering past wars that we are at once confronted with the profound horror and suffering of armed conflict and the increasing elusiveness of peace. The contributors to Bearing Witness do not presume to resolve these troubling questions, but provoke new kinds of reflection. They explore literature, the arts, history, language, and popular culture to move beyond the language of rhetoric and commemoration provided by politicians and the military. Adding nuance to discussions of war and peace, this collection probes the understanding and insight created in the works of musicians, dramatists, poets, painters, photographers, and novelists, to provide a complex view of the ways in which war is waged, witnessed, and remembered. A compelling and informative collection, Bearing Witness sheds new light on the impact of war and the power of suffering, heroism and memory, to expose the human roots of violence and compassion. Contributors include Heribert Adam (Simon Fraser University), Laura Brandon (Carleton University), Mireille Calle-Gruber (Université La Sorbonne Nouvelle), Janet Danielson (Simon Fraser University), Sandra Djwa (emeritus, Simon Fraser University), Alan Filewod (University of Guelph), Sherrill Grace (University of British Columbia), Patrick Imbert (University of Ottawa), Tiffany Johnstone (PhD Candidate, University of British Columbia), Martin Löschnigg (Graz University), Lauren Lydic (PhD, University of Toronto), Conny Steenman Marcusse (Netherlands), Jonathan Vance (University of Western Ontario), Aritha van Herk (University of Calgary), Peter C. van Wyck (Concordia University), Christl Verduyn (Mount Allison University), and Anne Wheeler (filmmaker).
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.398 | 0.131 |
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".