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Record W7130942302 · doi:10.22452/afkar.vol26no2.7

Reliability, Validity and Factor Structure of Fitrah Scale

2024· article· W7130942302 on OpenAlexaff
Akbar Husain, Fauzia Nazam, Mubashir Gull

Bibliographic record

VenueJurnal Akidah & Pemikiran Islam · 2024
Typearticle
Language
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsConestoga College
Fundersnot available
KeywordsInstinctScale (ratio)Reliability (semiconductor)Natural (archaeology)Measure (data warehouse)PopulationConsistency (knowledge bases)Internal consistency

Abstract

fetched live from OpenAlex

Abstract Fitrah, in Islamic psychology, refers to the innate disposition or natural state with which humans are created. This concept encompasses the inherent qualities of purity, morality, and the instinctive inclination towards faith in God. Understanding and measuring fitrah is crucial as it plays a significant role in shaping individual behaviors and overall well-being. Despite its importance, there has been a lack of standardized tools to assess fitrah comprehensively. This study aims to standardize a self-report measure of fitrah with a theoretical and empirical foundation applicable to a diverse Muslim population across various contexts and rooted in the behavioral psychological tradition. The article described the development and preliminary psychometric properties (i.e., reliability and validity) of the Fitrah Scale, an 18-item measurement with a 5-factor structure comprising Beatitude, Moral Uprightness, Devoutness, Innate Goodness, and Faith in God. The Fitrah Scale has shown good content, factorial validity, and internal consistency reliability (i.e., Cronbach’s alpha).

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)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.328
Teacher spread0.300 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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