MétaCan
Menu
Back to cohort
Record W2469109289 · doi:10.1177/0030222815612786

Coping With Imminent Death: Thematic Content Analysis on Narratives by Japanese Soldiers in World War II

2015· article· en· W2469109289 on OpenAlexaff
Enoch Leung, Amanda Chalupa

Bibliographic record

VenueOMEGA - Journal of Death and Dying · 2015
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoping (psychology)ObedienceThematic analysisNarrativePsychologyContent analysisMilitary serviceSocial psychologyClinical psychologyQualitative researchPolitical scienceSociologySocial scienceLawLiterature

Abstract

fetched live from OpenAlex

Coping affects somatic and psychological outcomes. This article explores narratives in a book, Kamikaze Diaries: Reflections of Japanese Student Soldiers, which report on the ways of coping used by each kamikaze participant before and during military service. The purpose of this study is to observe the possibility of a trend in coping strategies and consider how these trends inform us about other populations facing imminent death. This study analyzed data and extracted meaning from the narratives in the book (thematic content analysis). Within the thematic content analysis, the Ways of Coping scale was used, which describes the coping strategies people use when facing problems. The most frequently used coping strategies before they entered the military were "Accept Responsibility," "Endurance/Obedience/Effort," and "Self-Control," while once in the military, they were "Accept Responsibility" and "Endurance/Obedience/Effort." All the coping strategies used by kamikaze pilots appeared to focus on the passive self, which may be the type of coping in other populations facing death.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.344
Teacher spread0.257 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueOMEGA - Journal of Death and DyingSame topicGrief, Bereavement, and Mental HealthFrench-language works237,207