FACTORS AFFECTING STUDENTS’ ENROLMENT IN URBAN AND REGIONAL PLANNING HIGHER INSTITUTIONS OF LEARNING WITHIN ENUGU STATE, SOUTH EAST OF NIGERIA
Bibliographic record
Abstract
This study is aimed at determining the factors that are responsible for negative development in students’ enrolment as well as improving the popularity of Urban and Regional Planning among students during their admission. Urban and Regional Planning or Town/City/Physical Planning is a coordinating discipline concerned with spatial ordering of land uses in every environment. The research adopted survey method. Through this, quantitative analysis technique was utilized in the questionnaire representative samples data that generalized the population. The results of the students’ enrolment in Urban and Regional Planning are generally considered very poor. From the results in the table and bar chart, the Factor 1(F1): Geography as a compulsory subject for admission into Urban and Regional Planning affects students’ enrolment most with 88%. This is followed by Factor 5 (F5): Fear of unemployment after graduation. The third in affecting the enrolment is Factor 2 (F2): Course’s inadequate information; while the worst insignificant in affecting enrolment is Factor 6 (F6): High cost of the study which has 52%. The next insignificant is Factor 4 (F4): Candidates being Scared of Design with 61%, and barely insignificant being Factor 3 (F3) which is ignorance of the course’s graduates having 63%. It therefore recommended for remove of Geography as a compulsory subject for admitting students into the course study, providing solution to fear of unemployment or underemployment after graduation, sensitizing and educating people properly about the course.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".