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Record W2343386083 · doi:10.51644/9780889209428

Long Night’s Journey into Day

2006· book· en· W2343386083 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsMoonlight

Abstract

fetched live from OpenAlex

Sickness, starvation, brutality, and forced labour plagued the existence of tens of thousands of Allied POWs in World War II. More than a quarter of these POWs died in captivity. Long Night’s Journey into Day centres on the lives of Canadian, British, Indian, and Hong Kong POWs captured at Hong Kong in December 1941 and incarcerated in camps in Hong Kong and the Japanese Home Islands. Experiences of American POWs in the Philippines, and British and Australians POWs in Singapore, are interwoven throughout the book. Starvation and diseases such as diphtheria, beriberi, dysentery, and tuberculosis afflicted all these unfortunate men, affecting their lives not only in the camps during the war but after they returned home. Yet despite the dispiriting circumstances of their captivity, these men found ways to improve their existence, keeping up their morale with such events as musical concerts and entertainments created entirely within the various camps. Based largely on hundreds of interviews with former POWs, as well as material culled from archives around the world, Professor Roland details the extremes the prisoners endured — from having to eat fattened maggots in order to live to choosing starvation by trading away their skimpy rations for cigarettes. No previous book has shown the essential relationship between almost universal ill health and POW life and death, or provides such a complete and unbiased account of POW life in the Far East in the 1940s.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0070.006
Open science0.0010.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0630.028

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 head0.030
GPT teacher head0.237
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2006
Admission routes1
Has abstractyes

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