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Record W2550428584 · doi:10.5539/jel.v6n1p143

Perceptions of Education Role in Developing Society: A Case Study at Riau, Indonesia

2016· article· en· W2550428584 on OpenAlexvenueno aff
Sri Yuliani, Dicki Hartanto

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationGovernment (linguistics)PerceptionPopulationDescriptive statisticsDescriptive researchLocal governmentPsychologySocioeconomicsMedical educationPolitical scienceSociologyEconomic growthSocial sciencePublic administrationDemographyMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the role of education and how the involvement of governments, companies and communities themselves in promoting education in the area, which in this case took the case to at Riau, Indonesia. Total population of this research was that people in the district of Pinggir Bengkalis, and the samples were 24 people who were in the villages of Pinggir. The research methodology was descriptive quantitative. The research data was collected through questionnaires and documentation. The calculation of data indicated that the public perception on role of education in promoting the society generally looked very good with most of percentage above 90%. Then, the achievement showed good results in the involvement of the company in advancing education. Then, the government has completed educational facilities in the area according to public perception showed the lowest yield with enough categories or 65.8%. The average score showed that the role of education was in very good category or 90.5%. Therefore, it certainly needs to be given priority in local government.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.362
Teacher spread0.334 · 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

Citations17
Published2016
Admission routes1
Has abstractyes

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