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Record W4200012001 · doi:10.18280/ijsdp.160713

Implementation Challenges of Land Redistribution Programme in South Africa

2021· article· en· W4200012001 on OpenAlexvenueno aff
Nwabisa Tyekela, Christopher Amoah

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsRedistribution (election)BeneficiaryLand reformGovernment (linguistics)Economic growthLand registrationInjusticeLand tenurePoliticsBusinessPolitical scienceEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.049

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.258
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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