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Record W2150016039 · doi:10.5430/wje.v5n1p102

Industrial Training Programmes of Polytechnics in Ghana: The Pertinent Issues

2015· article· en· W2150016039 on OpenAlexvenueno aff
Kwabena Nduro, Isaac Kofi Anderson, James Adu Peprah, Frank B. K. Twenefour

Bibliographic record

VenueWorld Journal of Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Thematic analysisCompetition (biology)On-the-job trainingJob trainingEngineeringBusinessEngineering managementOperations managementManagementPedagogyPsychologySociologyEconomic growthEconomicsQualitative researchVocational educationSocial science

Abstract

fetched live from OpenAlex

In today’s world of stiff competition in the labour market, coupled with advanced technology, industries require ofstudents to have job experience before employing them. The challenge here is that the experience being required isnot taught in the lecture rooms. The reality is that, it is only gained though hands on the job, thus real worldconfrontation popularly called industrial attachment a platform for students arm themselves with all the skill,knowledge and demanded experience. This article examines the industrial attachment programmes of polytechnics inGhana and pertinent issues involved. The paper tackled the problem from four thematic areas; the preparation beforethe training starts, the perceived challenges encountered, the benefits derived from embarking on the training andsuggested strategies to be employed to enhance the programme.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.152
GPT teacher head0.415
Teacher spread0.263 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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