A Comparative Study of Language Instructional Delivery System between Nursery Schools in Rural and Urban Areas in Osun State
Bibliographic record
Abstract
The study compared the language instructional delivery system between the nursery schools in rural and urban areasin Osun state. The population consisted of all the nursery school teachers in Osun state. Proportionate randomsampling was used to prune down the population. In all 130 nursery school teachers, 68 in urban areas and 62 in ruralareas served as respondents of the study selected from 30 nursery schools located in rural and urban areas. Aresearcher made instrument named Teachers’ Questionnaire (TQ) was designed for data collection. Thequestionnaire was validated before use and the reliability coefficient yielded 0.85 which is significant at 0.05 level ofsignificance. Data were analysed statistically using t-test analysis and percentages. Findings showed that there wasno significant difference between the methods adopted for implementing language curriculum in nursery schoolslocated in both rural and urban areas. A significant difference existed between the human resources available innursery schools located in both rural and urban areas. It was also found out that there was no curriculum for Yorùbálanguage in most of the nursery schools visited. The study concluded with recommendations for both proprietors ofnursery schools in rural and urban areas to improve on their present standard in terms of teaching personnel,instructional resources and teaching/learning process, nursery school teachers and the three-tiers of government.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".