A pilot study for measuring roadway exposure through GPS watches worn by bicycle messengers and food delivery workers during work shifts
Bibliographic record
Abstract
We conduct a pilot study on the delivery trips of bike messengers and food delivery workers using GPS-derived data to understand these gig economy jobs. Between July and September 2018, 19 workers were equipped with GPS watches for two consecutive days (n=38 participant-days). One-second signal data was classified using an algorithm to identify idle time periods between trips. This enabled us to extract times, speeds and distances on the road, as well as idle time blocks and as a share of total work shift. Extrapolated data on number of deliveries is compared with exit interview recalls as benchmark. Workers travel on average 31km (SD=13.3km) in shifts of over 5 hours (316 min., SD=84.4 min.) and conduct on average 20 deliveries (SD=5.8). They spend on average 10 min. bouts waiting for food or packages (SD=2.8) and spend on average 36% (SD=11%) of their work shifts on the road. The pilot provides important information on shift characteristics and deliveries and indicates the importance of time idling waiting for packages. This suggests greater per time and per kilometer injury risks than could be estimated when assuming workers are always on the streets. In a context of commission-based work, idling also reduces potential wage.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".