What Can We Do to Improve Urban Goods Movement Data Collection in Canada? Findings of the TAC Project on the Framework for the Collection of High Quality Data on Urban Goods Movement
Bibliographic record
Abstract
Urban goods movement contributes significantly to a region's economic development and wellbeing. However, much less attention is paid to this contribution, and to goods movement's impact on urban transportation problems and solutions, than to passenger movement. Related to this is the relative paucity of data that characterize urban goods movement. To this end, in 2006 the Transportation Association of Canada (TAC) initiated a project that aims to develop a framework for collecting urban goods movement data. The work was divided into two phases. Phase 1, completed in late 2007, reviewed existing data sources and identified needs as expressed in the literature. Phase 2 was initiated in late 2008. It administered a user needs survey to Canadian governments, participants in the supply chain and selected academics and others who are involved with urban goods data. Phase 2 also followed up with some recent developments in Canada and overseas, in order to develop the final study product: [a] a framework for collecting high quality urban goods data, and [b] a strategy for implementing the framework. This paper reports on the key findings of Phase 2, specifically, the framework and the strategy for implementing it. The paper also identifies immediate, relatively low-cost actions that could do much to improve the state of urban goods movement data quickly and broadly.
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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 | 0.091 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.038 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".