Bibliographic record
Abstract
The winter months in the City of Toronto’s Downtown Yonge Business Improvement Area (“DYBIA”) are a time of reduced pedestrian activity, decreasing from 130 thousand per week in the summer to 90 thousand per week in the winter" (Diego, 2022), and the lowest consumer spending for the city (“Animating Winter in Toronto: First Steps - Update,” 2019). This study examines how different cities, as well as planning organizations, have improved the economy, equity and culture during the winter months using winter placemaking. A literature review was conducted on the topics of winter city planning and placemaking across North America. Qualitative research was accumulated through 11 semi-structured interviews with 12 professionals who are involved in winter placemaking or winter planning, either as city staff employees or planning professionals. The findings from these interviews supplemented by a policy scan were used to create six case studies on the cities of Montréal, Winnipeg, Regina, Saskatoon, Edmonton, and Toronto. The interviews were then compared to find common themes of how to improve the winter months in cities. The common themes are winter culture, winter design and winter equity. The outcome of the research includes recommendations on how the DYBIA and the City of Toronto could improve winter culture, winter design and equity within the DYBIA and the City.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".