Um panorama da previdência social dos trabalhadores da Economia Gig do setor de transporte no Brasil
Bibliographic record
Abstract
Technological advances have been transforming work relationships. Non-traditional work relationships are growing and conquering the space of traditional contracts. Consequently, ensuring adequate coverage of social protection systems for these evolving work relationships becomes a pressing challenge. This study undertakes an estimation of Gig Economy workers within Brazil’s transport sector, using microdata from the Continuous National Household Sample Survey (Pnad Contínua) by the Brazilian Institute of Geography and Statistics (IBGE). The primary aim is to furnish valuable insights for the formulation of public policies geared towards enhancing social security inclusion. The estimation methodology pivots on two principal variables: occupation and the economic activity undertaken by these workers. The findings underscore that during the third quarter of 2022, the total count of Gig Economy workers in the transport sector surged to 1.7 million individuals; remarkably, a mere 23% of this cohort contributed to the Social Security system.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".