Older Informal Workers in the COVID-19 Crisis
Bibliographic record
Abstract
In the study, older informal workers are defined as those aged 60 or older. Approximately 13 per \ncent (288) of the sampled informal workers are older workers, with 63 per cent of them women \nand 37 per cent men. Men are over-represented among older workers as their share within this age \ngroup is higher than among the younger workers surveyed. \nThe number and percentage of older workers varies significantly between cities and sectors as \nshown in Figures 1 and 2. Older workers made up a larger proportion of the sample population \nin New York and Pleven but had much less presence in the samples from relatively youthful cities \nsuch as Dakar and Dar es Salaam. Sectoral differences were also seen, with older workers noticeably absent in categories of work such as motorcycle drivers and massage therapists in Bangkok, \nand kayayei (headload porters) in Accra.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".