Perspectives on Stock Theft Prevention in the Selected Provinces of South Africa: Failures and Successes
Bibliographic record
Abstract
Residents of Limpopo (LIM) and KwaZulu-Natal (KZN) Province are witnessing higher rates of stock theft, with the inhabitants of the selected communities living in fear for the prevention of this scourge. This study explores the extent of this crime in the selected areas of LIM and KZN, considering contributory factors, determining the relationship between the South African Police Service Stock Theft Units (SAPS STUs) and other relevant stakeholders, as well as looking at existing strategies (And their failures and successes) in responding to this crime effectively. A qualitative research approach coupled with Non-probability: Purposive sampling was used in this study. The targeted population consisted of 113 participants. For data collections, Focus Group Discussions (FGDs), Key Informant Interviews (KIIs), and Observation Schedules were adopted. lack of appropriate preventative measures has led to rise of stock theft, it was, therefore, discovered that both the affected livestock farmers and members of the community lost confidence toward the police, Besides, the perspectives on stock theft prevention in LIM and KZN reflect a greater challenge, with inadequate solutions present, since the current preventative measures are ineffective. Thus, understanding stock theft phenomenon is critical to its prevention as the sector of livestock in South Africa is the contributory key to the value of the agricultural economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".