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Record W4312679056 · doi:10.17509/re.v2i1.46767

STRENGTHENING THE TAHFIZ STUDY SYSTEM IN THE ERA OF THE INDUSTRIAL REVOLUTION 4.0: DIRECTIONS AND CHALLENGES IN MALAYSIA

2022· article· en· W4312679056 on OpenAlexaff
Muhammad Iqbal Bin Samadi, Dian Widiantari, Azmil Hashim

Bibliographic record

VenueReligio Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsCurriculumGovernment (linguistics)EmpowermentPersonalityInstitutionIndustrial RevolutionPolitical scienceHuman capitalSpace (punctuation)ManagementSociologyEngineering ethicsEngineeringPublic relationsEconomic growthPedagogySocial sciencePsychologyEconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

The high acceptance and response of the public to tahfiz education has provided space for the government to provide various alternatives to tahfiz education through the approval of the Ulama Council to implement the National Policy on Tahfiz Education (DPTN). The main purpose of this research is to see how far the development of tahfiz education and soft skills (KI) among huffaz are. Related to that, the four curriculum modules that will be introduced are Tahfiz Turath, Tahfiz Sains, Tahfiz Dini and Tahfiz Kemahiran. Among the main directions and challenges that need to be faced are the combination of ideas, competitive infrastructure and a strong implementation commitment to ensure that the Tahfiz institution continues to excel on the world stage. In the end, empowerment of the education system and tahfiz studies can produce professional huffaz who have a balance of religious knowledge, secular academics, personality and technology skills simultaneously in developing holistic human capital in the current era of the Industrial Revolution 4.0

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)0.002
metaresearch head score (Gemma)0.000
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 score0.060
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.068
GPT teacher head0.320
Teacher spread0.252 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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