Bibliographic record
Abstract
Global Positioning System (GPS)-based travel diaries have emerged as valuable tools for urban transportation planning but have had little uptake in rural transportation planning. This chapter describes the methodology and effectiveness of employing vehicle-instrumented passive GPS units and participant-prompted recall with Geographic Information Systems (GIS) in a rural travel diary study focused on understanding older driver travel behaviour. A convenience sample of 60 rural older drivers in New Brunswick, Canada participated for an average of 5.3 days. The GPS devices recorded 1649 “stops” of 1 minute or more, with 8% of all “stops” due to stoplights or traffic delay. Remaining “stops” were organized into 1494 trips (one origin with one destination), with participants supplying travel purposes and driver and passenger details for 99.1% of trips. An external battery for the GPS unit minimized satellite acquisition delay but was exhausted in 10% of cases. Results from the study permitted an exploratory analysis of the impact of select license restrictions on older drivers, the potential for rural older drivers to meet their needs without a car, and exposure analysis by road class.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".