Bibliographic record
Abstract
The main objective of this paper is to pursue a comparative study of urban land use and transportation policies using selected Canadian and Korean cities. To this end, the paper addresses a set of interrelated questions: What has been the effect of changes in the structural pattern of developments on travel patterns, and on factors such as land use? How do these relationships vary between urban areas? Over the next decade, what prospects do traditional and innovative land use and transportation policy measures have in accommodating growth, maintaining mobility while conserving resources in different urban areas? What is the scope for the transferability of innovative policies between different cities and between different countries such as Canada and Korea? The approach of the paper combines a review and synthesis of available land use and transportation information sources, a series of interviews with planners and a brief survey of planning documents to assess the effectiveness of different policy instruments in different city contexts. More specifically, this paper establishes a common framework for comparing the characteristics of land use and transportation on the basis of information derived from published reports, transportation studies and city plans. The Canadian cities of Toronto, Ottawa-Hull, Calgary and Vancouver and Korean cities of Seoul and Inchon are initially compared on the basis of quantitative characteristics in population and employment, transportation demand and supply, as well as transportation system performance. The criteria of selecting these six urban areas from both countries are based on the urban structure and availability of information. The potential transferability between urban areas of traditional and innovative transportation and land use policy instruments relevant to the next decade was also a consideration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.056 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".