MétaCan
Menu
Back to cohort
Record W1848295290 · doi:10.5539/ass.v11n16p74

Technical Skills Evaluation Based on Competency Model for Human Resources Development in Technical and Vocational Education

2015· article· en· W1848295290 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsVocational educationHuman resourcesFunction (biology)Engineering managementQuality (philosophy)Knowledge managementOrder (exchange)Human resource managementCompetency assessmentCompetence (human resources)Computer scienceProcess managementBusinessMedical educationEngineeringPsychologyManagementPedagogyMedicine

Abstract

fetched live from OpenAlex

The purpose of this paper is to advance discussion of the function of the competency model for the technical skills evaluation and preparation of human resource and workers in organization. Human resources development is one of the important elements that determine the status of a country, whether it is recognized as a developed, developing or underdeveloped country. To realize it vision to be a developed country by the year 2020, Malaysia had planned, carried out and developed its human resources through Technical and Vocational Education (TVE). The competency-based education, which has been introduced in TVE, is a new approach in producing not only quality and expert human resources but also technical workers that possess high competency in behavioral and thinking with regard to technical tasks. A few competency models can be applied as evaluation and assessment system in order to evaluate the technical competency of human resources. Model for Human Resource Development (HRD) Practice is proposed to determine the evaluation and assessment system that can gauge worker competency in carrying out tasks related to technical skills.

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.945
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.069
GPT teacher head0.410
Teacher spread0.341 · 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