Bibliographic record
Abstract
The study examined the attitude of students towards technical education in Osun State, Nigeria. Descriptive research method was adopted by the investigator. The population of the study was made of 350 students comprising 200 males and 150 females. One validated research instrument was used to gather data. One research hypothesis was answered in the study. The results showed that: male students were more favourably disposed towards Technical Education since there was a significant difference between their mean attitude scores(4.008); students from rural setting had a higher disposition toward Technical Education as compared with their counter parts from the urban setting(4.912); the composition of the students showed a positive disposition with no much difference between their mean attitude scores; the t-value computed to establish the relationship between attitude and performance levels of students showed a negative correlation (t = -1.48); in spite of geographical location there was no marked difference in the attitude and interest of students for Technical Education. Almost half of the colleges do not have enough competent instructors. Among others, it was recommended that the state government should make efforts to purchase more teaching and learning aids for effective teaching in technical colleges. Comprehensive in-service programme must be devised by government for practicing instructors to update their knowledge in their various area of specialization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".