Infrastructure development within a regulated environment: Concerns for regulators
Bibliographic record
Abstract
Poor delivery of infrastructure leads to inefficient pricing of these assets, which is passed through to consumers. Inefficient pricing is caused by a poor selection of a funding and financing method as well as project overruns. This article used a case-study approach to investigate if South African (SA) infrastructure projects were executed efficiently. It was found that the procurement method was not a reason for inefficient infrastructure delivery. Further, SA projects overran significantly by between 5 and 58%. The case of Transnet’s pipeline project was highlighted. Two case studies (Gautrain and e-tolls) are presented to highlight issues around funding. It was found that the user-pays mechanism of funding is efficient only if there is complete transparency and communication between the user of the infrastructure and other stakeholders. Given the findings, this paper ends with policy recommendations for regulators of utilities that will ensure that consumers are protected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".