Examining the South African labour market during the COVID-19 lockdown period
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.
Bibliographic record
Abstract
This study analysed the 2020 first quarter to 2022 second quarter waves of the Quarterly Labour Force Survey (QLFS) data and all five waves (2020–21) of the National Income Dynamics Study – Coronavirus Rapid Mobile Survey (NIDS-CRAM) data to examine the South African labour market outcomes during the COVID-19 lockdown period. The QLFS data showed that low-educated Africans aged 25–44 years and those involved in low skilled occupation categories were most vulnerable to job loss. The NIDS-CRAM data indicated that for those who still worked in February 2020, 51% worked all five waves, 14% worked in four waves and 9% worked in three waves. Only 0.5% and 1.8% turned out to be unemployed and inactive in all waves, respectively. For the February 2020 employed who lost their jobs and became unemployed in April 2020 (wave 1), 60% of them worked again but 22% remained unemployed in March 2021 (wave 5).
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it