Implementation Challenges of Land Redistribution Programme in South Africa
Bibliographic record
Abstract
Upon assuming political office, the ANC government instituted a land redistribution programme to address the land ownership injustice perpetrated during the apartheid regime whereby the non-white citizens owned only 7% of land in South Africa. However, the programme has not achieved the set target; thus, this study sought to understand the challenges curtailing the successful implementation of the programme. The study used a qualitative research approach. An in-depth interview was conducted with three purposefully selected senior officials from three Departments in Greater Kokstad Municipality involved in the land redistribution programme’s implementation. The findings indicate that the major issues curtailing the programme's implementation are land claim disputes and mediation process, reliance on the willing-seller-willing-buyer model, lack of institutional capacity, cumbersome beneficiary selection process, land beneficiary resettlement support, and inadequate programme’s monitoring and evaluation. There is an urgent need for the government to institute measures to address the challenges preventing the smooth implementation of the land redistribution programme in South Africa. These challenges prevent the programme’s beneficiaries from accessing the land, thus preventing them from experiencing socio-economic emancipation as promised.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".