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Record W2809842941 · doi:10.4000/archipel.624

Kekerasan Kemanusiaan dan Perampasan Tanah Pasca- 1965 di Banyuwangi, Jawa Timur

2018· article· id· W2809842941 on OpenAlexaff
Ahmad Luthfi

Bibliographic record

VenueArchipel · 2018
Typearticle
Languageid
FieldSocial Sciences
TopicAsian Studies and History
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Kebijakan Land reform di Banyuwangi era 1960-an berhasil meredistribusi tanah kelebihan maksimum, tanah absentee, dan tanah negara bekas perkebunan dan hutan. Pasca kudeta 1965 terjadi kekerasan kemanusiaan dan tindakan counter-Land reform oleh berbagai pihak. Didasarkan pada data-data utama dari kantor pertanahan dan dokumen militer Banyuwangi, artikel ini menunjukkan kaitan erat antara Land reform dengan korban pembunuhan yang menjadi sasaran pasca-1965. Artikel ini adalah usaha awal untuk membuka dimensi ekonomi-politik terhadap massacre pasca-1965. Sejarah agraria dan kekerasan kemanusiaan dalam artikel ini dibaca melalui konsep “primitive accumulation”, dalam argumen bahwa kekerasan kemanusiaan berupa pengusiran, perampasan tanah dan pembunuhan terhadap petani adalah tonggak baru dan bagian integral dari pembangunan politik dan ekonomi agraria Orde Baru berikutnya. Jika massacre adalah making die, pembunuhan dalam arti literal, maka proses yang terjadi setelahnya adalah letting die, berupa tercerabutnya mereka dari sistem produksi (tanah) dan kewarganegaraannya.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0220.002

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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