Bibliographic record
Abstract
In 2008 and 2009, Clean Air Partnership (CAP) conducted two research studies on Bloor Street in Toronto, Ontario, Canada. These studies were designed to determine the public acceptability and potential economic implications of reallocating road space from on-street parking to widened sidewalks or bike lanes. CAP released two in-depth research reports (2009, 2010) about these studies. This paper summarizes the main findings of each. In July of 2008, 61 merchants and 538 patrons on Bloor Street in the Annex neighbourhood of downtown Toronto were surveyed. In July of 2009, the study was replicated on the same street but in a different location further from the downtown: 96 merchants and 510 patrons on Bloor Street in Bloor West Village were surveyed. Overall support for changes in street use allocation was greater in the Bloor Annex neighbourhood than Bloor West Village. However in both neighbourhoods, the majority of merchants believed that changes to accommodate an increase in pedestrian or cyclist infrastructure would increase or would not change their daily number of customers. In both neighbourhoods, walking was the dominant reported mode of travel to Bloor Street (46 % of the patrons surveyed in both study areas). Bicycling was more common in the Annex, and driving was more common in Bloor West Village. In terms of patron preferences for changes to street use allocation, bike lanes were preferred over widened sidewalks in both neighbourhoods. In Bloor West Village, there was almost equal patron preference of change and no change, whereas in the Bloor Annex neighbourhood, surveyed patrons indicated a preference for a change of the street use allocation by a ratio of nearly 4 to 1.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.502 | 0.393 |
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".