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Record W2566631668 · doi:10.5430/wje.v7n1p1

Views of Teacher-Trainees on Clothing and Textiles Education in two Teacher Education Universities in Ghana

2016· article· en· W2566631668 on OpenAlexvenueno aff
Phyllis Forster, Rosemary Quarcoo, Elizabeth Lani Ashong, Victoria Ghanney

Bibliographic record

VenueWorld Journal of Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInternshipClothingSubject (documents)Teacher educationPsychologyPedagogyHigher educationScale (ratio)Mathematics educationMedical educationPolitical scienceMedicineLibrary science

Abstract

fetched live from OpenAlex

The study explored the views of teacher-trainees on Clothing and Textiles (C&T) education in two teacher educationuniversities in Ghana. The objectives were to find out whether pre-tertiary Sewing/C&T lessons provided them withsmall-scale business skills, and foundation for higher education, they could teach Sewing/C&T competently oninternship, identify their sources of motivation, and elicit their suggestions to improve on the subject. Data collectedrevealed that one-tenth and two-thirds acquired small-scale business skills from their lessons at basic and secondarylevels respectively. About 71% indicated they taught the subject competently on internship and career intention wastheir main motivating factor for the subject. For improvement, there is need to provide adequate modern facilities andcompetent teachers with current ideas in content and pedagogy, and good teacher relationship with students andindustry. Other suggestions were, students should remain focused and attract others to the subject through theirdressing. Feed-back from the teacher trainees indicates that Sewing/C&T education in Ghana provides occupationalskills and foundation for further studies.

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)0.002
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.027
GPT teacher head0.357
Teacher spread0.330 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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