MétaCan
Menu
Back to cohort

Protecting Human Rights amidst Poverty and Inequality: The South African Post-apartheid Experience on the Right of Access to Housing

2008· article· en· W2070403052 on OpenAlexvenueno aff
John C. Mubangizi

Bibliographic record

VenueAfrican Journal of Legal Studies · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsPovertyDemocracyState (computer science)Political scienceConstitutionPoliticsDevelopment economicsEconomic growthInternational human rights lawPopulationFundamental rightsInequalityLawSociologyEconomics

Abstract

fetched live from OpenAlex

Abstract A significant gain of the new political and constitutional dispensation ushered in South Africa in 1994 was a commitment to the protection of human rights. However, protecting human rights in a country where the gap between the rich and the poor is among the largest in the world was always going to be a daunting challenge. The challenge is even more daunting with the protection of socio-economic rights such as the right of access to adequate housing. This article explores the challenges that South Africa faces in protecting human rights in the face of persistent poverty of over half of the country's population, vast economic disparities and gross inequality. Focusing on the right of access to adequate housing, the author explores some prospects arising from the roles played by the constitution; domestic courts; other state institutions as well as non-state actors. The article concludes that although the challenges are real, the prospects are promising. However, a lot must be done if the democratic miracle that has characterized South African society over the last fifteen years is to be maintained.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.037
Scholarly communication0.0060.006
Open science0.0010.010
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.109
GPT teacher head0.368
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations13
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueAfrican Journal of Legal StudiesSame topicLegal Issues in South AfricaFrench-language works237,207