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Record W3092413731 · doi:10.47655/dialog.v34i1.149

REFLEKSI UNTUK MODERASI ISLAM-INDONESIA

2017· article· en· W3092413731 on OpenAlexaboutno aff
Nanang Tahqiq

Bibliographic record

VenueDialog · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and Radicalism
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianIslamPolitical radicalismTerrorismViolent extremismMedia studiesPolitical scienceGender studiesPsychologySociologyHistoryLawPoliticsPhilosophy

Abstract

fetched live from OpenAlex

This reflective article describes the author's experience of the common attitude of Indonesian Muslims who are basically uncourageous and afraid of murder, violence, terrorism, radicalism, or the like. Indonesian Muslims prefer to moderate attitude than extreme one. Therefore, Indonesian Muslims--both individual and communal-- will always be moderate from the first onwards. Both experiences while living abroad (Canada) and notably in the country (Indonesia) proved to the author that Indonesian Muslims did not like violence. Moreover, the evidences suggested that the source of violence is external influence. One of related experiences on how Indonesian Muslims abroad tend to avoid violence was also experienced by the author during his lecture at McGill University, Montreal, Canada. In this article the author sketches briefly his story and conclude that the basic characteristics of Indonesian Muslims is moderate, and moderate Muslim trends or movements will be well acceptable and grow up.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.042
GPT teacher head0.370
Teacher spread0.328 · 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 designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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