Relevance of the Senior High School Curriculum in Ghana in Relation to Contextual Reality of the World of Work
Bibliographic record
Abstract
The mass unemployment of the youth toady has mostly been attributed to the irrelevance of the school curriculum. However, the skills in the curriculum have not been subjected to critical analysis to empirically prove their relevance or otherwise. The purpose of the study was therefore to identify the skills embedded in the curriculum, those skills the learners have acquired and those that employers usually demand of employees by relating them to empirical findings of the skills employers in general demand of employees. A conceptual content analysis was used to determine the skills embedded in the curriculum. Purposive sampling procedure was used to select twenty-one students and fourteen key informants for an interview. The data from the interview were sorted out into themes and coded through the use of NVivo 8 to help in the counting of frequencies of each skill. It was found out that the senior high school curriculum, though was generally rated as relevant, the skills with the highest frequencies in the curriculum focused on attitudes and values while those required by employers focused on the application of knowledge. On the basis of these findings, it can be concluded that the curriculum is relevant in instilling values into the students but it is not relevant in the application of knowledge that employers usually demand of employees at the work environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".