In search of quasi-subordinate workers in China: A typology of gig riders by economic dependency and subordination
Bibliographic record
Abstract
The employment status of platform workers has generated debate both in China and internationally. Drawing on legal thresholds from selected developed countries and statistical indicators developed by Eurofound and Eurostat, our study constructs context-specific indicators for identifying quasi-subordinate workers in the Chinese labour market. Between December 2021 and January 2022, we distributed online questionnaires in five Chinese cities to measure the subordination levels of 7,680 platform gig riders. Workers were classified into subtypes based on two dimensions: economic dependency and personal subordination. Our results indicate that only around 19 per cent of gig riders can be classified into the existing “employee versus independent worker” binary framework, while the rest should be grouped into a new “quasi-subordinate” worker category. Statistical analysis reveals significant differences in working conditions across subgroups. Our results suggest that high economic dependency is a predictor for longer working hours, greater work intensity and increased perceived pressure, while high personal subordination is related to low job satisfaction. Social insurance coverage is found to be particularly inadequate among subgroups with higher levels of subordination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".