Bibliographic record
Abstract
The paper assessed the competency framework perceived by technical and vocational education and training (TVET) lecturers in Nigerian tertiary institutions of Bauchi State.The inevitable needs to investigate the lack of competent TVET lecturers resulted in the production of quack TEVT graduates in Nigerian educational system.The paper reviewed existing literature on competency models with a view to proposing a resilient competency framework for TVET lecturers in Bauchi State Nigeria.The material and methods were carried out using secondary data and were precisely and critically analysed to come up with reliable results.From the foregoing, a conceptual framework had been proposed which will be tested in the study area in order to determine the suitable items of the competency framework for TVET lecturers in Nigerian tertiary institutions.Four major group of competency have been identified to be relevant in measuring TVET lecturers' competency which are organisational competency, thinking competency, application competency and TVET lecturer competency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.999 | 0.995 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".