Implementation of Visuals Arts (Fined and Applied Arts) as Vocational Programmes in Tertiary Institutions: Problems and Prospects
Bibliographic record
Abstract
<p>Over the years, Nigeria and indeed world leaders have been battling to combat the raging poverty and unemployment rates. Although vocational education has been identified as a panacea to these, significant studies reporting obstacles to the implementation of visuals arts as vocational education programs in tertiary institutions have remained grossly insufficient. This is the issues addressed in the study. Survey research design was adopted while 200 purposively selected art education and vocational education respondents from the University of Nigeria, Nsukka, provided participated in the study. Among others, that the respondents agreed that the problems facing the implementation of visual art in tertiary institutions as a vocational education program range from poor awareness to lack of parental support. Further studies examine from students’ perceptive, on how best to implement visuals arts as vocational education programs are recommended.</p>
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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) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".