MétaCan
Menu
Back to cohort
Record W3113148279 · doi:10.6000/1929-4409.2020.09.163

Multicultural and Multilingual Inside Education Perspective

2020· article· en· W3113148279 on OpenAlexvenueno aff
Arfin Sudirman, Muhammad Ihsan Dacholfany

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Education and Local Wisdom
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismMulticultural educationSociologyMultilingualismInterpreterPerspective (graphical)DemocracyCultural competenceSocial scienceEpistemologyPedagogyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

The purpose of the description of this study is to create a just and democratic safe society; security, justice and democracy can be achieved by diagnosing conflict as sublimation for differences in the language and culture of society. So that multicultural, multilingual education at least minimizes local, national and global community noise in overcoming vertical-horizontal conflicts. This study uses methods and techniques for analyzing general domain cultural themes to cultural sub themes; linear system relationships between components in education. The concept of multicultural, multilingual conflicting heterogeneity communities can help multicultural learning with the cultural-lingual approach; multimedia, multimetodic, multisite against the conflict of the heterogeneity community typically in the islands of the coast. Conflicting heterogeneity society is used as multicultural media learning media diagnostic material. Contrastive analysis of language and cultural conflicts is very helpful in diagnosing sublimation in an educational perspective. Hopefully the concepts of multicultural and multilingual community conflict in heterogeneity contribute in an educational perspective, namely multicultural and multilingual synthesis of local, national, and global in society elements.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.297
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.089
GPT teacher head0.421
Teacher spread0.332 · 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.

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Criminology and SociologySame topicMulticultural Education and Local WisdomFrench-language works237,207