Odds stacked against workers: datafied gamification on Chinese and American food delivery platforms
Bibliographic record
Abstract
Abstract This article presents a cross-national comparative study examining how American and Chinese platform companies approach the gamification of on-demand food delivery. The study, based on ethnographic fieldwork in New York City and Beijing, shows how couriers in these cities negotiate the gamified app-based systems designed to convince them to log in and keep working. We argue that such systems are not only a salient form of ‘algorithmic management’—as has been argued before—but also demonstrate the central importance of datafication within the organizational strategies of food delivery companies operating under conditions of financialized platform capitalism. Indeed, the deeply financialized nature of the on-demand food delivery industry creates conditions in which companies experiment with data-driven gamification techniques in an effort to manipulate their flexible labor supply in an agile and cost-effective way—to thereby elicit higher productivity and meet expectations of investors and shareholders. Our comparative analysis challenges assumptions of a universal mode of gamification and highlights the differences between such situated techniques and their impacts on workers, identifying two distinct design approaches that we term ‘Deal or No Deal’ in New York and ‘Grab-and-Stack’ in Beijing.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".