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Record W2945178708 · doi:10.3167/9781782383468

WEARY WARRIORS: Power, Knowledge, and the Invisible Wounds of Soldiers

2023· book· en· W2945178708 on OpenAlexfundno aff
Pamela Moss, Michael J. Prince

Bibliographic record

VenueBerghahn Books · 2023
Typebook
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsnot available
FundersUniversity of Victoria
KeywordsMasculinityPower (physics)BureaucracyMemoirState (computer science)PoliticsHierarchyGovernment (linguistics)TRACE (psycholinguistics)PsychoanalysisPolitical scienceCriminologyMedia studiesPsychologyGender studiesSociologyLaw

Abstract

fetched live from OpenAlex

As seen in military documents, medical journals, novels, films, television shows, and memoirs, soldiers’ invisible wounds are not innate cracks in individual psyches that break under the stress of war. Instead, the generation of weary warriors is caught up in wider social and political networks and institutions—families, activist groups, government bureaucracies, welfare state programs—mediated through a military hierarchy, psychiatry rooted in mind-body sciences, and various cultural constructs of masculinity. This book offers a history of military psychiatry from the American Civil War to the latest Afghanistan conflict. The authors trace the effects of power and knowledge in relation to the emotional and psychological trauma that shapes soldiers’ bodies, minds, and souls, developing an extensive account of the emergence, diagnosis, and treatment of soldiers’ invisible wounds.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.994
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.018
Scholarly communication0.0050.005
Open science0.0000.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.025
GPT teacher head0.321
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreOther

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".

Quick stats

Citations15
Published2023
Admission routes1
Has abstractyes

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