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Record W2018714327 · doi:10.1163/170873812x626441

Building a South African Human Rights Culture in the Face of Cultural Diversity: Context and Conflict

2012· article· en· W2018714327 on OpenAlexvenueno aff
John C. Mubangizi

Bibliographic record

VenueAfrican Journal of Legal Studies · 2012
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHuman rightsInternational human rights lawLinguistic rightsFundamental rightsPolitical scienceRights of NatureCultural relativismLawRight to propertyCultural diversitySociology

Abstract

fetched live from OpenAlex

Abstract South Africa has faced enormous challenges since the advent of democracy in 1994. One of the difficulties in the post-apartheid era has been the building of a human rights culture in the context of substantial cultural diversity. In this paper, the constitutional, judicial and institutional contexts – which have consolidated and supported the expression of human rights in the face of cultural diversity – are reviewed. The focus on cultural rights in the constitution is discussed, and the relevance of several constitutional institutions in terms of ensuring human rights, is mentioned. With a clear understanding of the constitutional, judicial and institutional contexts in place, the paper discusses the potentially inherent conflict between human rights and cultural rights, using gender-related issues as a proxy. Several examples of this potential conflict are discussed, including female circumcision, virginity testing and polygamy. The importance of human rights education for informing the debate about cultural and human rights in South Africa is emphasized. The answers to the challenges associated with the clash between cultural rights and human rights are not simple, although pragmatically – in addition to the role of the available constitutional, judicial and institutional structures – they could reside in a cross-cultural debate.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.069
GPT teacher head0.336
Teacher spread0.267 · 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 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

Citations6
Published2012
Admission routes1
Has abstractyes

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