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Record W2272888327 · doi:10.55016/ojs/ajer.v61i2.55709

Educational Practices for a New Nigeria: An Exploratory Study

2016· article· en· W2272888327 on OpenAlexvenueno aff
Hasan Aydın, Stephen Lafer

Bibliographic record

VenueAlberta Journal of Educational Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkProsperityFocus groupSyllabusDemocracyContent analysisQualitative researchSociologyPedagogyOpenness to experienceCurriculumSocial sciencePolitical sciencePsychologyLawSocial psychology

Abstract

fetched live from OpenAlex

This article reports on a qualitative study conducted at the Nigerian Turkish International Colleges (NTICs) in Abuja, Nigeria. Twenty-two participants comprised of three administrators, seven teachers, four parents, and eight students participated in the study. The data collected through observations, informal, formal and semi-structured in-depth individual interviews, focus groups, document analysis (of teachers’ syllabi, coursework materials, Nigerian nation-wide exam reports, and copies of district and state lesson design guidelines), and field notes were used for content analysis. Themes of the study were constructed to explore the schools’ role in promoting openness, mutual understanding, and habits of discourse vital to democracy in a society that is deeply divided along religious, ethnic, and geographical lines. This article explains the value of NTICs by focusing on the role of curriculum in promoting tolerance, unity, economic prosperity, and stability. This article also considers how these NTICs attempt to encourage the establishment of a civil society based upon democratic principles of governance while focusing on multicultural values.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.011
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.004
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.283
GPT teacher head0.553
Teacher spread0.270 · 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

Explore more

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