What Is a Midsize City? A Transportation Policy-Based Framework for Classifying Cities
Bibliographic record
Abstract
Midsize cities face a number of sustainability challenges, particularly in terms of transportation and land use, however only a small subset of the literature has attempted to address these issues. Examination of the state of the art in midsize city research reveals two obvious reasons for this: there is no consensus on a framework for defining midsize cities, nor is there an empirical understanding of the characteristics of midsize cities. This paper addresses both of these issues from the transportation planning perspective by providing an evidence-based definition for midsize cities in Canada, which highlights their unique travel behavior characteristics. Although the premise of this exercise is fairly simple, it contributes to practice in a number of ways. Most importantly, it establishes a common framework for Canadian urban policy-makers and researchers to use in communicating, sharing and comparing their work. Secondly, it allows Canadian municipalities to understand their peers, and to measure their progress according to their size and functional characteristics. Finally, it demonstrates a method for classifying and comparing municipalities, which may be used to develop a similar framework in other countries. The results of the Canadian urban classification analysis prove that midsize cities are indeed uniquely automobile-centric, and that over 37% of Canadians currently live in midsize cities. Given this, it is crucial that researchers and policy-makers turn their attention to midsize cities and develop policy tools that are tailored to these municipalities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".