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Record W4310208866 · doi:10.1177/14661381221134435

Transnational giving between Shikoku, Japan and Burma/ Myanmar: From memorializing One’s dead to humanitarianism with peace and war reflections

2022· article· en· W4310208866 on OpenAlexaff
Millie Creighton

Bibliographic record

VenueEthnography · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBuddhismMilitarismMilitarizationState (computer science)Government (linguistics)BurmeseWorld War IIAssertionRefugeeNarrativeSociologyConstitutionAncient historyLawPolitical scienceHistoryPoliticsArchaeology

Abstract

fetched live from OpenAlex

Bagan, Myanmar (formerly Burma) is famous for its over 2200 Buddhist temples. People contribute to these temples as charitable work, to fulfill social or sacred obligations, or show they are “good Buddhists”. In World War II Japan’s military government sent Shikoku youth to South East Asia, including Burma, where over 6000 died. Following WWII, Shikoku groups sent funds to Burma to memorialize their dead. Thus began over 70 years of transnational giving involving construction and maintenance of temples, generalized support, and bringing medical advances to Burma/Myanmar. This article explores Shikoku-Myanmar transnational giving, and how it reverberates with peace and war issues. It raises a counter narrative to the Japanese state’s assertion that Yasukuni Shrine is necessary to memorialize war dead, makes links with Japanese citizens’ movements upholding Japan’s pacifist constitution and Article 9 (renouncing militarism), and adds to gift-giving frameworks, showing how, once established gift-giving can create obligations including those not directly about reciprocation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.318
Teacher spread0.266 · 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 teacher head, 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
Published2022
Admission routes1
Has abstractyes

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