Food warriors: app-based delivery on electric micromobilities
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
Electric micromobilities (EMMs), including electric bikes, standup kick-style electric scooters, and electric unicycles are highly efficient and low impact modes for urban food delivery. However, the mobility they and their associated algorithmic platforms afford is implicated in a set of work practices and relations that reinforce precarious employment outcomes. Our interviews, observational and autoethnographic research in Vancouver, Canada, revealed that food delivery platforms promise flexibility and high earnings while motivating workers to toil for variable and low wages and engage in high-risk behaviour. We focused on food delivery workers using EMMs because barriers to accessing an EMM are lower than for a car, while affording greater mobility on congested city streets, incurring no parking fees, and delivering zero emission operation. However, ostensibly low financial barriers to entry mask the requirement for considerable knowledge of, and navigational skills within, the physical and virtual environments that workers must master to resist the control exercised by platforms (apps) in an intensely competitive playing field. App-based food delivery using EMMs implicates workers in a game that requires upfront investment, skill and the navigation of risk. It is a stacked game, in which mostly the house wins.
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 | 0.000 | 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