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A Study on Japanese Death Culture: Using the Movie Departures as an Entry Point

2025· article· W4415917306 on OpenAlexaff
Shangzhi Hu

Bibliographic record

VenueCommunications in Humanities Research · 2025
Typearticle
Language
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsMeaning (existential)Context (archaeology)Subject (documents)InstitutionalisationGlobalizationPopulationMeiji period

Abstract

fetched live from OpenAlex

This paper takes the Japanese film Departures as a point of entry to explore the multiple dimensions of contemporary Japanese death culture. In particular, it examines cremation as the dominant funerary practice in Japan, situating it within broader processes of institutionalization and social transformation surrounding death, religion, and social relations. The discussion highlights shifting attitudes toward religion to provide a comprehensive context for understanding how "death" has been perceived, regulated and practiced in Japan since the Meiji era. Furthermore, the paper approaches the subject from a socio-economic perspective, paying close attention to changes in family structure and the challenges of population aging. Taken together, this analysis demonstrates how Japanese death culture is being reconstructed at the intersections of tradition and modernity, religion and secularity, as well as individual and collective values. In the 80 years after World War II, with the development of the nuclear family and the aging population with fewer children, the Japanese people's views on life and death became increasingly diverse. It is of great significance for people to accurately understand the profound meaning of Japanese death culture for the diversified development of Japanese society and the integration of foreign talents under the background of globalization in the 21st century.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.351
GPT teacher head0.504
Teacher spread0.153 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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