The policing of illegal mining in Gauteng
Bibliographic record
Abstract
The aim of this study was to explore how the South African Police Service (SAPS) deal with illegal mining activities in Gauteng. The rising unemployment rate in South Africa and economic uncertainties in the neighbouring countries drive people to participate in illegal mining activities as a means of survival. In South Africa alone, the unemployment rate has reached 32 per cent in the fourth quarter of the year 2020. As such, illegal activities (such as illegal mining) have become one of the sources of income for many unemployed people in the country including people from African countries who reside in South Africa. Therefore, it is the intention of this research study to explore how SAPS deal with illegal mining activities in Gauteng \nThe study followed a qualitative research approach because this approach produces rich and detailed information about people’s knowledge and experience. Through purposive sampling, the researcher identified the relevant research participants who possess the information needed to answer the research questions. The topic was then explored using Focus Group Discussions (FDG’s), one-on-one interviews, and participant’s observations. \nThe study discovered that illegal mining is a process that involves men, women and sometimes children. Many people involved in illegal mining in South Africa are South African citizens and foreign nationals from different ethnic groups partaking in various roles in the mining process. Often the poor black men are the ones risking their lives by extracting the products from the mines, while the rich, black, white and Indian men are the buyers who, through their connections, will sell such products to the merchants dealing with copper, gold and diamonds. The study also discovered that the lived experiences of illegal miners are constantly associated with numerous challenges whereby they stay underground in extremely hot temperatures for days or weeks at times. Such spaces are used for different activities including cooking, sleeping and due to lack of proper sanitation, even as a place to relieve oneself. \nThe study further proves that there are numerous risks involved in illegal mining activities such as intergroup conflict, murder, attempted murder, rape and assault. The main challenge facing police is that the strategies they use, such as Disruptive operations, are ineffective in curbing the problem of illegal mining in this country as the problem continues. Owing to that, this study recommends that despite the damages that occur in the formal mining sector specializing in gold, the government need to decriminalized illegal mining activities in South Africa. The decriminalization process will enable the miners to get police protection, the government to establish policies that can effectively regulate illegal mining and for the miners to have access to the trade market as well as an improved relationship with the community members.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".