Using GPS technology to track hitchhiker activity in Northern BC
Bibliographic record
Abstract
Understanding the demographics of hitchhiking can help inform agencies involved in hitchhiking risk and crime prevention to make evidence-based decisions. Using dash-mounted GPS devices designed specifically for this purpose, we partnered with five courier companies to collect temporal and spatial data on hitchhiking activity in northern British Columbia between June 2012 and October 2014. Data collected were: location, time of day, gender, and whether or not the hitchhiker was alone (or in a pair or group). Some citizen science GPS data were also gathered (February to November 2012), along with hitchhiker interview information collected by local highway patrol officers. A total of 775 records revealed that the largest number of hitchhikers in northern British Columbia were First Nations and male between 20 and 49 years of age. Our data suggest that most hitchhikers traveled alone, in the summer, and in the early evening hours. Findings from our study have been mapped and provided to the local Office of the Highway Patrol for risk and crime prevention purposes.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".