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Record W4308460179 · doi:10.26522/ssj.v16i3.2755

Setbacks and Partial Victories: Social Justice Struggles After 28 Years of Democracy in South Africa

2022· article· en· W4308460179 on OpenAlexvenueno aff
Mondli Hlatshwayo

Bibliographic record

VenueStudies in Social Justice · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyUnemploymentDemocracyContext (archaeology)Language changeState (computer science)Political scienceEconomic JusticeGovernment (linguistics)InequalityEconomic growthDevelopment economicsPolitical economySociologyLawEconomicsPoliticsHistory

Abstract

fetched live from OpenAlex

Post-apartheid South Africa is ravaged by crises of extreme unemployment, poverty, and inequality. While the majority who were politically excluded by apartheid can now choose their government through democratic elections, social and economic justice continues to elude them. Neoliberal policies which seek to reduce state expenditure on social services and promote state policies that protect the interests of big businesses at the expense of working-class and poor communities, along with corruption and abuse of power, are the primary causes of poverty and unemployment. However, what is missing in the assessments of social justice since the pre-1994 democratic era is the recognition that social justice organisations have not simply disappeared, but have actually remained involved in social justice struggles. Based on information from in-depth interviews and internet sources, this article records some of the partial victories scored through these struggles, albeit in the context of generalised pauperisation of working-class and poor communities. These partial victories in the era of defeats show that these organisations, although weakened, have not given up the struggle for social justice.

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)0.001
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 score0.282
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.074
GPT teacher head0.309
Teacher spread0.236 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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