Delivery workers and the interplay of digital and mobility (in)justice
Bibliographic record
Abstract
On-demand delivery services are experiencing a moment of expansion, which the COVID-19 pandemic contributed to foster. For cities in quarantine, these services allow the supply of food and other primary goods without moving from home, making riders move and access them on behalf of the clients. During a pandemic, working as a rider potentially increases the risks of an already precarious job given the contractual arrangements and the algorithmic control that characterize this gig economy sector. We argue that platforms have generated forms of injustice that are reproduced and amplified by digital platforms encoded in the Global North, which are governed by regulations and optimization criteria that do not dialogue with the precarious reality of Global South cities. Focusing on the case of Santiago de Chile, our analysis draws on the triangulation and complementarity of two instruments: interviews before the COVID-19 pandemic and surveys involving riders during the COVID-19 pandemic. Our findings show that platforms generate specific forms of injustice that affect riders and their mobility in particular. The COVID-19 pandemic worsened such forms of digital injustice, increasing the pressure for constantly working and the exposure of riders to threats such as accidents, criminality and health risks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".