Data collection strategies for benchmarking urban goods movement across Canada
Bibliographic record
Abstract
Despite increasing recognition of the importance of goods movement in urban areas, and a growing number of related data collection efforts, Canada's systems for moving goods in urban areas are still poorly understood. The purpose of this study is to identify and evaluate options for a benchmarking system for urban goods movement. This paper characterizes the dimensions of goods movement that occur in urban areas, identifies performance indicators that reflect policy goals and objectives, summarizes the data requirements of state of practice urban goods movement modeling approaches, and assesses available data collection methods in Canada. The paper then outlines five alternative frameworks, each of which identifies a suite of data collection methods that, in combination, has potential to fulfill data needs for measuring performance indicators and supporting modeling for predicting those indicators. The paper concludes that a national shipper-based survey and periodic purchase of vehicle tracking data from 3rd party providers provides, on balance, a preferable combination of complementary strengths for nation-wide performance benchmarking of urban goods movement.
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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 | 0.059 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".