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Record W4313469079 · doi:10.1080/0376835x.2022.2163227

Investigating volunteer activities in South Africa

2023· article· en· W4313469079 on OpenAlex
Jaydro Fondling, Simbarashe Murozvi, Derek Yu, Nothando Mtshali

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDevelopment Southern Africa · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsVolunteerQuarter (Canadian coin)UnemploymentUnemployment rateSocioeconomicsDemographyVolunteer workDemographic economicsGeographyWork (physics)SociologyEconomic growthPolitical scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

This is the first South African study that analysed all three available waves of Statistics South Africa’s Volunteer Activities Survey data, which was linked to the Quarterly Labour Force Survey in the third quarter of the same year (2010, 2014 and 2018). The empirical findings showed that volunteers were predominantly female Africans without Matric, aged 25–34 years and resided in the urban areas of KwaZulu-Natal, Gauteng and Limpopo. In 2018 the labour force participation rate and unemployment rate of the volunteers were 62% and 34% respectively. These rates were both a bit higher than the corresponding rates of people who did not volunteer. The volunteers spent 20 h in the past four weeks on volunteering activities relating to service work and elementary occupations. More than 85% of volunteers did not expect to receive anything back. For those who indicated otherwise, they most likely expected to receive out-of-pocket expenses and food.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.058
GPT teacher head0.281
Teacher spread0.223 · 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