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Record W2489481623 · doi:10.5539/ies.v9n8p60

Investigation of Historical Characters in Republic of Turkey Revolution History and Kemalism Course Books (1993-2012)

2016· article· en· W2489481623 on OpenAlexvenueno aff
Mustafa Safran, Cengiz Dönmez, Kubilay Yazıcı, Barış Çiftçi

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologySociologyMathematics educationHumanitiesSocial sciencePedagogyPsychologyPolitical scienceArtPoliticsLaw

Abstract

fetched live from OpenAlex

T.R. Revolution History and Kemalism courses have a crucial function in our educational system in terms of making people embrace governmental ideology, teaching them recent national history, and constituting citizenship acquisition. In constituting the acquisition of behavioural and cognitive changes in these three target areas, the topics that are covered in T.R. Revolution History and Kemalism course books, and format and presentation of these topics have altered in time, whilst the existence of historical characters have not changed. This study is a qualitative study, and the data were gathered through document analysis. The study presents important data about historical characters that take place in T.R. Revolution History and Kemalism course books published in 1993-2012. The results of the study show that the number, frequency and use of historical characters in each book are different.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.201
GPT teacher head0.408
Teacher spread0.207 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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