From Flexible Labour to ‘Sticky Labour’: A Tracking Study of Workers in the Food-Delivery Platform Economy of China
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
Despite considerable scholarly attention to the proliferation of gig work on digital platforms, research tracing the broad trends of labour relations is scant. Analysing interview and survey data on food-delivery workers in China between 2018 and 2019, this article demonstrates a trend of de-flexibilisation for workers, which contradicts the purported flexibility of platform-mediated work. It is argued that de-flexibilisation is achieved through intertwined labour management tactics, technological engineering, and the cultural normalisation of platform-dependent precarious jobs. Platform companies and third-party staffing agencies have jointly deployed algorithmic systems and communicative techniques to cultivate what we refer to as ‘sticky labour’. The study contributes to the current debate on working in platform capitalism by weighing the compound effects of labour management strategies, social impact of technological engineering of the work process, and the cultural normalisation of platform work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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