Travel patterns and challenges experienced by University of Johannesburg off-campus students
Bibliographic record
Abstract
When universities across the world emerged, the majority of students were provided with oncampus accommodation. However, with the increase in the number of universities, students seeking to enter universities and the decline in university funding, the result was an increase in the number of students residing off-campus. This lead to more limited social-contact opportunities with other students, which are vital for the enhancement of their learning and development. It also resulted in off-campus students spending a considerable amount of time travelling to and from university. This study aimed to investigate the travel patterns, characteristics and challenges faced by University of Johannesburg off-campus students by ascertaining inter alia: the means of transport used; travel time; the views of students in regard to the challenges they face; and possible improvements thereto. A quantitative approach was predominantly used to collect data from students by means of a questionnaire and this was supplemented with focus group discussions on two campuses. The study results revealed that off-campus students experience considerable challenges accessing campuses.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".