Understanding the Movement of Goods, Not People: Issues, Evidence and Potential
Bibliographic record
Abstract
The movement of goods or freight occurs in every urban area. However, such movements have not received the same level of attention as that given to the movements of people. We do not understand the effects of several massive changes affecting urban freight movements. This paper presents a comparative review of planning studies of the movement of goods in order to assess our current understanding and to provide a context for a research agenda on the movement of urban goods. The studies reviewed provide basic data such as time of day activity, trip and vehicle characteristics. However, they do not provide an understanding of the key relationships that underlie the movement of goods, including those related to land use/transport interaction. Basic research into an integrated modelling effort for urban goods movement (UGM) is needed to address issues associated with congestion and air quality that affect most major urban centres.
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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 | 0.034 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.030 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".