Bibliographic record
Abstract
War-related wounds to the body, mind, and soul of military personnel in historic and current times can hardly be ignored.Yet the cause, significance, and treatment of combat trauma remain hotly disputed a er centuries of debate.Military psychiatry has been the predominant site where these disagreements play out, primarily because military psychiatrists are the fi rst to see a soldier with combat trauma.Cultures, nation-states, and societies more generally shape the way in which traumatized soldiers are treated medically and socially, and supported fi nancially, and are (not always) welcomed home.In this book we tease out some of the issues important in the ways in which soldiers and veterans become done in, disenchanted, and worn out-that is, how they become weary warriors.Each of us brings a diff erent set of interests to this project.Pamela Moss is trained in social and cultural geography, although she primarily works in interdisciplinary se ings.Conceptually, her interests in experience, space, and power have led her to feminist theoretical frameworks that focus on women, resistance, and illness.She is most interested in those concepts that assist in teasing out the unremarkable, mundane acts people do that can challenge existing fi gurations of power and knowledge.Empirically, Pamela's research takes up discursive constructions and material practices of the subject, body, and self in various contexts-as in medical diagnostic practices, song lyrics, and her own experiences as an academic (Moss 2011(Moss , 2013a;; Moss and Teghtsoonian 2008).Pamela's interest in traumatized soldiers arose from a conversation she had with an elderly man who had been a German prisoner of war (POW) held by Canadian soldiers during the Second World War.Michael J. Prince is trained in political science, public administration, and policy analysis, and has conducted research in areas of welfare state programs and services for a range of groups, including persons with dis-
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.428 | 0.248 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".