Temporal arbitrage, fragmented rush, and opportunistic behaviors: The labor politics of time in the platform economy
Bibliographic record
Abstract
This article examines how on-demand service workers on digital platforms make and live their time in the case of China’s food delivery industry. Using ethnographic data, the study elucidated multiple facets of couriers’ temporality in their struggle to meet the exacting delivery time imposed by platforms while moving through biased urban spaces as marginalized temporal subjects. It is argued that a new temporal order, referred to as temporal arbitrage in this study, has been normalized in the recent platform economy. It shifts the customer’s cultural expectation to on-demand service at the expense of an increasingly hectic tempo for the workers. We demonstrate the mundane, and sometimes opportunistic, tactics deployed by workers to reconstruct their temporality. The article connects the workers’ temporality to the urban spaces, digital work process, and socioeconomic structures. It fills an important research gap by addressing the under-explored yet essential temporal dimensions in the expanding “just-in-time” labor force.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".