Identifying transport policy gaps in student travel demand management in South Africa
Bibliographic record
Abstract
Background: Travel demand in higher education precincts is derived from the affordability of university education, the availability of student accommodation on- or off-campus and the manner in which university mobility is managed.Objectives: This article described the transport policy environment for student travel behaviour through the process of integrated policy analysis (PIPA) with the primary aim of outlining the major directions of student mobility management from peer-reviewed literature.Method: Gaps in the South African transport policy environment were identified for university student mobility as a result of the official policy position neglecting the segment and 7 of 26 public universities acting upon these markets without enabling legislation.Results: It was found that measures associated with managing travel demand demarcate mobility management practices. Through the literature, the article found that (1) the policy environment lags behind university interventions, which resonate with international evidence; (2) international evidence reveals that multiple directions for managing travel demand for university precincts; and (3) there is a need to reform the mobility and access policies for university precincts in South Africa (SA).Conclusion: In essence, the literature review presented heterogenous contexts and techniques to specify mobility and access problems and redress them. This enhanced the quality of policy design, evaluation and implementation particularly for integrated transport planning in SA. The primary limit of this study was that it is a policy review, relying heavily on secondary data to set the scene for future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".