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Record W2746711119 · doi:10.5539/ass.v13n9p167

The Law Politics in Indonesia’s Pancasila and Citizenship Education Curriculum Revitalization of 2013

2017· article· en· W2746711119 on OpenAlexvenueno aff
Maryanto Maryanto, Nor Khoiriyah, Supriyono Purwo Saputro

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Curriculum and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCitizenshipPoliticsPolitical scienceSociologyLawPedagogy

Abstract

fetched live from OpenAlex

The curriculum is one component of education which is very important. It acts as a guide that program implementers use in real learning process. Dynamically, curriculum always change in order to lead to the improvement of the education system. On the subjects of Pancasila and Citizenship Education in schools for example, has undergone several changes in the curriculum, both in charge of its content and on its nomenclature, this change is needed in order to create a future generation that had Pancasila character embedded on their heart as stated in national education goals. However, in the process of Pancasila and Citizenship Education's curriculum change, it is undeniable that it is influenced by the current Law Politics as politics is what makes the rules. But what exactly is the influences of the law politics on Indonesia’s Pancasila and Citizenship Education Curriculum Revitalization Of 2013?. The studies conducteed on how the Law politics influence the revitalization of Pancasila and Citizenship Education curriculum showed that (1) the Directions of Pancasila and Citizenship Education curriculum revitalization in Indonesia leads to; (2) The basic foundation establishment and implementation of the curriculum in 2013's Pancasila and Citizenship Education subject (3) the Products of Pancasila and Citizenship Education curriculum revitalization in Indonesia. The result that is a model of a good Pancasila and Citizenship Education curriculum revitalization should be implemented, monitoring and evaluation needs to be done to determine the level of achievement and expected results. Monitoring and evaluation results will serve as a recommendation to develop and or improve curriculum that will come.

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)0.006
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 score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.003
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 head0.030
GPT teacher head0.392
Teacher spread0.363 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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