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Record W2146996084

Indexing Creativity Fostering Teacher Behaviour: Replication and Modification

2015· article· en· W2146996084 on OpenAlexvenueno aff
Ayhan Dikici, Kaycheng Soh

Bibliographic record

VenueHigher education of social science · 2015
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishCreativityReplication (statistics)Reliability (semiconductor)PsychologyFactorialMathematics educationConfirmatory factor analysisIndex (typography)Data collectionSearch engine indexingValidityComputer scienceStatisticsSocial psychologyStructural equation modelingArtificial intelligenceMathematicsPsychometricsLinguisticsMachine learningWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

the literature. One of these scales is Creativity Fostering Teacher Behaviour Index (CFTIndex) developed for Singaporean teacher originally. It was then translated into Turkish and trialled on teachers in Nigde province with acceptable reliability and factorial validity. The main purpose of this study is to compare the original English and the translated Turkish versions and to explore more flexible use of CFTIndex to facilitate data collection for future research. Factor loadings of English and Turkish versions are found to be highly similar. The original version that consists of 45-item was shortened to 27-item and also grouped into five sets. Correlations with the full-length version show acceptable validity and reliability. All new versions were verified by confirmatory factor analysis. It is concluded that CFTIndex has flexible features and its shorter forms can be used with confidence. It is especially useful when a study entails collecting data for many variables.

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)0.064
metaresearch head score (Gemma)0.142
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.142
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.005
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.175
GPT teacher head0.458
Teacher spread0.283 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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