Internet Platform Employment in China : Legal Challenges and Implications for Gig Workers through the Lens of Court Decisions
Bibliographic record
Abstract
Research Objective and Questions We aimed to examine court rulings on disputes between network platforms and labour providers in order to understand the nature of the employment relations and the broader consequences for society as a whole. We addressed two questions : Methodology We primarily used secondary data, namely 102 publicly available Court decisions from 2014 to 2019. The case decision reports were downloaded from the Supreme People’s Court “Network of Court Decision Papers.” Results Disputes occurred mainly in cities that have the most developed platforms and an independent worker model of employment. They mainly involved network platforms that provide such services as driving, food delivery and courier services. All of the disputes involved road accidents, and over half occurred in Beijing and Shanghai—two leading cities in China that have dense populations. Dispute cases rose sharply, peaked in 2017, started to drop in 2018 and fell even more in 2019. The disputes seem to have educated people on both sides, with the result that more precautions are being taken. Contributions Our study makes three contributions. First, we identified three types of platform employment in China, the motives of the platforms in their choice of labour utilization and the legal implications in terms of labour and third-party protection. Second, we examined the attitude and role of the courts in judging disputes between network platforms and labour providers within legal constraints. Third, we propose that socialization of contract service should be central to platform employment.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".