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Record W3213164891 · doi:10.6000/1929-4409.2021.10.147

Understanding Indonesian People through Literature: Indigenous Psycho-Sociology Perspectives

2021· article· en· W3213164891 on OpenAlexvenueno aff
Anas Ahmadi, Darni Darni, Bambang Yulianto

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Language Analysis
Canadian institutionsnot available
FundersDirektorat Riset dan Pengabdian Masyarakat
KeywordsIndigenousIndonesianInterpretation (philosophy)SociologyPolitenessPerspective (graphical)Character (mathematics)LocalityLiterary criticismEpistemologySocial sciencePsychologyAestheticsLinguisticsPhilosophyArtVisual arts

Abstract

fetched live from OpenAlex

Indigenous studies are currently attracting humanities researchers, one of which is the field of literature. Literary researchers explore the locality contained in literary texts through the perspective of indigenous studies. In this regard, this study explored Indonesian literature through the perspective of indigenous studies. The theory used in this study was literary criticism associated with indigenous psychology and indigenous sociology. The data source used was the Rafilus novel written by Budi Darma. The research method used was qualitative because the researchers emphasized the interpretation of the text. Data analysis techniques included the stages of indexation, reduction, exposure, and interpretation. The results showed that the character Rafilus displays the psychological side of indigenous people of Java through segmentation: friendliness dan politeness in life, self-awareness in life, a simple life desire, and loves to learn.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0090.018
Scholarly communication0.0090.009
Open science0.0010.005
Research integrity0.0010.003
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 head0.105
GPT teacher head0.324
Teacher spread0.219 · 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
Published2021
Admission routes1
Has abstractyes

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