Electricity Theft in South Africa: Examining the Need to Clarify the Offence and Pursue Private Prosecution?
Bibliographic record
Abstract
Electricity theft is one of the challenges with which South African government-owned power-distribution company Eskom is grappling. Eskom has lost billions of rands in annual revenue owing to electricity theft. Different strategies are in place to combat electricity theft. However, in South Africa, electricity theft is not a statutory offence. This contrasts with the approach adopted in countries such as China, Canada, India, Australia and New Zealand, where legislation provides for such an offence. Although electricity theft is not a statutory offence, prosecutors would like electricity thieves to be punished. In this context, there are conflicting High Court decisions on whether electricity theft is a common-law offence or indeed an offence at all. The purposes of this article are: to highlight the problem of electricity theft in South Africa and the conflicting jurisprudence from the High Court on whether electricity theft is an offence; to recommend that Parliament amend legislation to criminalise electricity theft specifically; and also to empower Eskom to institute prosecutions against those who are alleged to have stolen electricity.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".