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Record W4395691351 · doi:10.1163/2208522x-bja10055

Emotions and Battlefield Medicine in the American Revolutionary War

2024· article· en· W4395691351 on OpenAlexaboutno aff
Chris M. Blakley

Bibliographic record

VenueEmotions History Culture Society · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Emotions Research
Canadian institutionsnot available
Fundersnot available
KeywordsResentmentSpanish Civil WarEnthusiasmPsychoanalysisBattlefieldPolitical scienceLawHistorySociologyMedicineManagementPsychologyAncient historySocial psychology

Abstract

fetched live from OpenAlex

Abstract This essay examines the expression of emotional states by frontline medics in the American Revolutionary War to understand how doctors felt about their role as caregivers during the Canada Campaign of 1775–76 and the Sullivan-Clinton Expedition of 1779. The first section queries how surgeons managed the emotional strain of caring for wounded and sick soldiers during the Invasion of Quebec. The journals of two doctors who expressed resentment and grief towards the officer corps during the invasion, Samuel Fisk Merrick and Lewis Beebe, are considered. In the second section, the essay focuses on the role that expressions of enthusiasm, particularly what scholars term settler colonial optimism, played in during a total war. Here another pair of doctors, Jabez Campfield and Ebenezer Elmer, afford insight into the hope surrounding land-grabbing felt by medics during the army’s genocidal campaign in western New York and Pennsylvania.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.018
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.282
Teacher spread0.234 · 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
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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