Towards a revitalization of passenger rail services in South African cities: Lessons from international institutional reforms
Bibliographic record
Abstract
For over a century passenger rail services in South Africa have been provided by one or other form of national public monopoly. Over the past decade these services have been in steep decline, effectively collapsing when COVID-19 lockdown regulations were imposed. Current policy attention is focussed on reviving operations and installing an institutional structure capable of providing ‘safe, reliable, effective, efficient and fully integrated’ rail transport. Key to this is a commitment to devolve functions to capable lower spheres of government. The aim of this paper is to explore lessons from international experiences in institutional reforms centred around devolution. To qualify for inclusion, cases needed to have experienced a deliberate policy action to devolve passenger rail functions from a national to a lower sphere of government. A literature search revealed 11 such cases. Case reviews focussed on: the circumstances of the devolution; any associated vertical separation and privatisation; impacts; and any subsequent policy reversals. Key lessons included: devolved operations should be accompanied by financial resourcing; vertical separation requires independent institutions capable of adjudicating competing interests; private sector participation in operations is less risky than private infrastructure ownership; and the devolution of both train and bus services can enhance modal integration.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".