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Record W2625485987

Violent, frequent and lengthy strikes in South Africa : is the use of replacement labour part of the problem?

2016· article· en· W2625485987 on OpenAlexaboutno aff
Karin Calitz

Bibliographic record

VenueSouth African Mercantile Law Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsOpposition (politics)Collective bargainingPolitical scienceLabor relationsLabour lawArbitrationLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

Frequent, protracted and violent strikes in South Africa are seen as a symptom of the failure of the country’s collective bargaining system. The proposed 2012 amendments to the Labour Relations Act (LRA), which aimed at preventing high strike levels and unacceptable behaviour during strikes, included a compulsory strike ballot and compulsory interest arbitration. These proposals were not included in the final version of the amended LRA because of opposition by the trade union federation Cosatu. This article endeavours to answer the question whether a ban on replacement labour could be conducive to peaceful industrial relations. Studies of the Canadian system indicated that there is no clear correlation between a ban on replacement labour and shorter and less frequent strikes, but there is strong evidence that a ban on replacement labour is conducive to generating less violent behaviour. In the light of the constitutional guarantee of the right to strike and the fact that there is clear evidence that replacement labour exacerbates violence during strikes, it is recommended that a ban be placed on replacement labour in South Africa.

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)0.001
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.253
Teacher spread0.219 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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