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Record W2732512101 · doi:10.5539/hes.v7n3p11

Preservice History Teachers’ Attitudes towards Identity Differences

2017· article· en· W2732512101 on OpenAlexvenueno aff
Fatih Yazıcı

Bibliographic record

VenueHigher Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)CurriculumDiversity (politics)PsychologyScale (ratio)Ethnic groupSocial psychologyHigher educationSociologyPedagogyMathematics educationPolitical scienceLaw

Abstract

fetched live from OpenAlex

The ongoing changes in history education in support of diversity have an effect on Turkey even if on a limited scale. Although the current history curriculum in Turkey promotes the identity transmission instead of respecting different identities, it also has some goals such as “teaching the students about basic values including peace, tolerance, mutual understanding, democracy, and human rights, and making them sensitive about maintaining and improving these values”, which is compatible with the contemporary understanding of history education. However, it must be noted that the attitudes and perceptions of teachers are as important as their presence in curriculum in terms of reaching the aims of history education. The aim of this study was to reveal preservice history teachers’ attitudes towards identity differences. Identity Attitudes Scale (IAS), which was developed by Yazici (2016) to measure the attitudes towards identity differences, was conducted on 314 preservice history teachers. Preservice teachers’ attitudes towards identity differences in terms of gender, and their ethnic, religious and political identities were examined using t-test and one-way variance analysis. As a result, it was found that the variables had effect on preservice teachers’ attitudes at varying rates.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.429
GPT teacher head0.510
Teacher spread0.081 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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