Modeling the job quality of ‘work relationships’ in China’s gig economy
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
Policy interventions geared toward improving the quality of gig economy work depend not only on how this work is classified in legal terms but also on a fine‐tuned understanding of the relevant factors that determine the quality of the gig ‘work relationship’. Models that are used to evaluate standard work, however, are poorly adapted to gig work. This article proposes a ‘work relationship’ model adapted to the gig economy. The model is inspired by Dunlop’s systems approach and is constructed from 24 in‐depth interviews with gig economy workers. A survey generated from the model was used to verify the relevance of 3 macro‐level and 12 micro‐level factors. Its main findings are that income, labour protections, voice and client behavior are the most significant factors in determining the quality of work and of work relations as determined by gig workers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it