Inevitability of Labour Broking in South Africa and the Need for Strict Regulation
Bibliographic record
Abstract
It seems apparent that despite all the agitations, protests, and concerns raised by various organised trade unions, non-governmental organizations (NGOs), scholars and interested persons on the need for South Africa to out rightly ban the business of labour broking in South Africa because of the various unfair labour practices being perpetrated by the labour brokers and their clients, the business continues to thrive and prosperous. The ban continues to fail because till date, no single legislation has been enacted specifically to outlaw labour broking. Therefore, it seems that labour broking as a business is inevitable in South Africa and will continue to operate. That being said, even if it is not banned, this article strongly accentuates the need to stringently regulate labour broking considering various unfair labour practices that labour brokers and their clients perpetrate against workers. Against the backdrop of this, the article extensively relied on and utilised the recently enacted Labour Relations Amendment Act, 2014 which makes a moderate attempt to protect casual workers from unfair labour practices in South Africa. The South African courts have made tremendous progress by interpreting and applying this regulatory regime to protect the labour broker's employees and transform labour broking in South Africa. This article contributes to the body of knowledge regarding the need to ensure holistic protection for vulnerable casual works through stringent regulation of the business. This assertion is made against the backdrop that this aspect has not been robustly researched hence this article seeks to address the problem and proffer solutions.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".