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Record W3181801752 · doi:10.1016/j.wss.2021.100044

“It's not enough:” Local experiences of social grants, economic precarity, and health inequity in Mpumalanga, South Africa

2021· article· en· W3181801752 on OpenAlexaff
Margaret S. Winchester, Brian King, Andrea Rishworth

Bibliographic record

VenueWellbeing Space and Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsMcMaster University
FundersNational Science Foundation
KeywordsPovertyPrecarityEconomic growthSocial protectionGovernment (linguistics)LivelihoodPolitical sciencePensionBasic needsDevelopment economicsEconomicsGeography

Abstract

fetched live from OpenAlex

South Africa has received international recognition for taking an active role in addressing extreme poverty by establishing a national social grant program. Lauded as one example to alleviate poverty, the operating assumption is that these strategies provide alternatives to mainstream development assistance. Notwithstanding their potential effects, the pathways generating livelihood change and their long-term implications for processes of citizenship formation and state society relations remain unclear. Drawing from an interdisciplinary study of social and economic change in Mpumalanga Province, South Africa, we analyze household surveys and qualitative interviews to examine how individuals manage their limited income through a balance of social grants, economic remittances, labor migration and strategic task-shifting. Though more than half of the households receive some form of pension support from the national government, many continue depending on remittances from household members living elsewhere. Social grants additionally interrelate with health maintenance in complicated ways, evidenced by high HIV rates within the study region. We argue that while the distribution of grants helps alleviate financial stress, the structure of assistance programs is more symbolically than materially significant for many families. Despite government assistance, families require social network mobilization and resources to access and secure healthcare and other basic needs.

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 score0.092
Threshold uncertainty score0.618

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 studies0.0010.000
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 head0.023
GPT teacher head0.293
Teacher spread0.271 · 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

Citations23
Published2021
Admission routes1
Has abstractyes

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