MétaCan
Menu
Back to cohort
Record W2759583517 · doi:10.5539/ies.v10n10p64

The Evaluation of the Opinions of Prospective Teachers about the Objectives of Human Rights Education

2017· article· en· W2759583517 on OpenAlexvenueno aff
Ramazan Özbek

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicValues and Moral Education
Canadian institutionsnot available
Fundersnot available
KeywordsDemocracyHuman rightsCurriculumScope (computer science)Human rights educationSociologyTeacher educationNonprobability samplingPedagogyPsychologyMathematics educationPublic relationsPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

The aim of this study is to evaluate opinions of prospective teachers attending Social Sciences Teaching Department Primary Education Section on the objectives of Human Rights Education in the scope of Citizenship and Democracy Education Curriculum. This study is vital for learning of democratic life. 25 prospective teachers studying in the 8th semester were selected by purposive sampling method. Prospective teachers’ opinions generally converge at the point where sufficient success is not being achieved in the realization of educational objectives. Other issues include human rights violations, causes of human rights violations, teaching the ways of protecting human rights, not only giving theoretical knowledge but also using different samples and methods in teaching activities with the help of technology, using environment, educating patriotic individuals who are sensitive to environment and human rights and willing to solve problems, educating individuals who can build and develop positive relationships with people in different races, thoughts and geographical regions, democratic individuals oriented to create a democratic society.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.156
GPT teacher head0.543
Teacher spread0.387 · 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 designQualitative
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

Explore more

Same venueInternational Education StudiesSame topicValues and Moral EducationFrench-language works237,207