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Record W2075029826 · doi:10.5539/ies.v6n11p171

Entrepreneurial Training Needs of Illiterate Women in Cross River State, Nigeria

2013· article· en· W2075029826 on OpenAlexvenueno aff
Emmanuel U. Ingwu, Stella-Maris Okey

Bibliographic record

VenueInternational Education Studies · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Education and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumVocational educationFocus groupPsychologyMedical educationTraining (meteorology)ClothingLivelihoodNeeds assessmentRural areaPedagogyAgricultureBusinessSociologyMarketingGeographyPolitical scienceSocial scienceMedicine

Abstract

fetched live from OpenAlex

In order to improve on the curriculum and participation rate of adult learners in the current Adult Basic Education (ABE) program in Nigeria, this explorative study investigated the entrepreneurial (or vocational) training needs of illiterate women in Cross River State (CRS). Three research questions were posed to elicit from the participants their demographic characteristics, perception of the ABE program and perceived entrepreneurial training needs. The descriptive-survey design was adopted for the study. The focus group discussion along with probing interview sessions were held with a sample of 240 women learners drawn from the urban and rural ABE centers. Frequencies, percentages and bar charts were used to report the data. The study shows that majority of the women are young, single, unemployed and dropped out of school and perceived the current ABE as not meeting their needs. Majority of the subjects desire training in home management, clothing and textiles. Those in the rural areas prefer training in agricultural production. A few of those in the urban centers prefer training in computer/secretarial related skills. The major conclusion of the study is that the learners (women) should be involved in identifying their learning needs that would at the end of the program empower them to improve on their livelihoods.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.057
GPT teacher head0.414
Teacher spread0.357 · 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 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

Citations5
Published2013
Admission routes1
Has abstractyes

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