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Record W2493980236 · doi:10.5206/cie-eci.v45i1.9283

Reviving the Muslim tradition of dialogue: A look at a rich history of Educational theory and institutions in pre-modern and modern times

2016· article· en· W2493980236 on OpenAlexvenueno aff
M. R. K. Afridi

Bibliographic record

VenueComparative and International Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipIslamFace (sociological concept)SociologySecular educationReligious educationSocial sciencePolitical sciencePedagogyLawTheologyPhilosophy

Abstract

fetched live from OpenAlex

This paper aims to generate a debate within Muslim scholarship and comparative educators to engage in analysing both the institutions and the philosophy of education in Islam historically, to understand its present challenges and to create an environment conducive to dialogue between various civilizations and educational systems. At present Muslim parents, teachers and students in contemporary educational systems face a big challenge. On one hand, a modified system of Western education is likely to leave Muslim children exposed to a set of an underlying set of secular values and assumptions which are alien to the spirit of Islam, but on the other hand Muslim schools of the old style seem unable to prepare children adequately for the needs of the modern world or to help them take part in the scientific, technological and economic progress (Halstead, 1995). At the core of this issue lies the lack of knowledge of both Western educators and contemporary Muslim theorists regarding the rich tradition of education and scholarship in Islam that ensured the coexistence of the religious and the secular through dialogue with other traditions.

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)0.008
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0130.060
Scholarly communication0.0120.013
Open science0.0010.006
Research integrity0.0040.005
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 head0.096
GPT teacher head0.387
Teacher spread0.292 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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