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Record W3013226225 · doi:10.26522/ssj.v2020i14.1971

Negotiated Precarity in the Global South: A Case Study of Migration and Domestic Work in South Africa

2020· article· en· W3013226225 on OpenAlexaffvenue
Zaheera Jinnah

Bibliographic record

VenueStudies in Social Justice · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPrecarityVulnerability (computing)Framing (construction)FormalitySociologyCitizenshipPrecarious workContext (archaeology)Political scienceGender studiesPolitical economyWork (physics)PoliticsGeographyLaw

Abstract

fetched live from OpenAlex

This article explores precarity as a conceptual framework to understand the intersection of migration and low-waged work in the global south. Using a case study of cross-border migrant domestic workers in South Africa, I discuss current debates on framing and understanding precarity, especially in the global south, and test its use as a conceptual framework to understand the everyday lived experiences and strategies of a group that face multiple forms of exclusion and vulnerability. I argue that a form of negotiated precarity, defined as transactions which provide opportunities for survival but also render people vulnerable, can be a useful way to make sense of questions around (il)legality and (in)formality in the context of poorly protected work, insecure citizenship and social exclusion. Precarity as a negotiated strategy shows the ways in which people interact with systems and institutions and foregrounds their agency. But it also illustrates that the negative outcomes inherent in more traditional notions of precarity, expressed in physical and economic vulnerability, and discrimination in employment relations, mostly hurt the poor. This suggests the importance of an intersectional approach to understanding precarity in labour migration studies.

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)0.002
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 score0.179
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.196
GPT teacher head0.469
Teacher spread0.273 · 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

Citations20
Published2020
Admission routes2
Has abstractyes

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