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Record W4212939939 · doi:10.25071/2563-2418.13

"MASA JEPANG" DAN TAMBANG MIKA:

2019· article· id· W4212939939 on OpenAlexvenueno aff
Lorraine V. Aragon

Bibliographic record

VenueLOBO Annals of Sulawesi Research · 2019
Typearticle
Languageid
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
FundersWenner-Gren Foundation
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Selama pendudukan dalam Perang Dunia II di Indonesia, tentara Jepang memaksa penduduk desa di dataran tinggi Sulawesi Tengah untuk mengolah tambang mika yang terletak di dekat desa Towulu' di bagian selatan kecamatan Kulawi. Penduduk dataran tinggi masih menyimpan ingatan-ingatan yang kuat tentang kesulitan-kesulitan pendudukan. Namun, mereka bingung mengapa tentara Jepang memper-budak mereka untuk mengekstraksi mineral mengkilap yang hanya digunakan penduduk setempat pada acara- acara ritual untuk menghias blus-blus dari kulit kayu. Pertanyaan-pertanyaan tentang minat pemerintah Jepang pada tambang di dekat desa Towulu diklarifikasi dengan sebuah pengujian tentang penggunaan-penggunaan industri mika untuk komponen-komponen elektronik dan produk-produk perang strategis lainnya. Gabungan antara peng-gunaan-penggunaan mika yang penting untuk tujuan-tujuan militer selama Perang Dunia II, pembatasan Sekutu terhadap impor mika ke Jepang, dan keharusan-keharusan kerja yang ketat dalam ekstraksi mika menempatkan tambang Towulu dan pen-duduk tawanannya yang gesit menjadi penting bagi kepentingan perang Jepang.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.276
GPT teacher head0.472
Teacher spread0.196 · 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 designObservational
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
Published2019
Admission routes1
Has abstractyes

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