Bibliographic record
Abstract
An average of 1.2% of work trips in Canada in 2001 were accounted for by bicycling, but this varied considerably by metropolitan area and by province. In this study, the authors chose six Canadian cities for detailed analysis of their bicycling trends and policies: Vancouver and Victoria in British Columbia; Montreal and Quebec City in Quebec; and Ottawa and Toronto in Ontario. All of these cities have made impressive efforts to encourage more and safer bicycling. Most of the cities report increases in bicycling levels over the past twenty years but appear to have reached a limit due to lack of financing for much needed bicycling infrastructure (bike lanes and paths, intersection modifications, parking, etc.). In addition, the low-density, automobile-oriented suburban sprawl spreading around most Canadian cities has been increasing trip distances, thus making bicycling less feasible outside the urban core. Finally, Canadian provinces and cities have not imposed any significant restrictions on car use or imposed increases in fees, taxes, and other charges for car use, such as most European cities have implemented in order to discourage driving and increase transit use, bicycling, and walking. If Canadian cities really want to further increase bicycling levels, they will have to further expand bicycling infrastructure, impose more charges and restrictions on car use, and curb low-density sprawl.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".