The availability of smoking-permitted accommodations from Airbnb in 12 Canadian cities
Bibliographic record
Abstract
PURPOSE: Airbnb is a web-based peer-to-peer (P2P) service that enables potential hosts and guests to broker accommodations in private homes as an alternative to traditional hotels. The hospitality sector has increasingly gone smoke-free over the last decade. This study identified the availability and cost of smoking-permitted accommodations identified on Airbnb. METHODS: The study team searched for Airbnb accommodations in 12 Canadian cities across each of Canada's 10 provinces. Searches included availability for a single person for a private room, or double occupancy for an entire home/apartment; searches were for 1-night and 1-week stays. RESULTS: Cities across Canada, including Regina, Fredericton and Charlottetown, had no smoking-permitted accommodations available for the searches conducted. The proportion of private rooms available for one night that permitted smoking ranged from 2% in Calgary, 4% in Winnipeg and St. John's, 10% in Halifax and Victoria, 18% in Toronto, 45% in Vancouver and 69% in Montréal. The average cost for a private room for one night in Vancouver was $128, while the cost for a private room that permits smoking was $62; however, in other markets prices were more similar. DISCUSSION: Across Canada, there is a wide range of smoking-permitted accommodations available through Airbnb. In some markets, smoking-permitted accommodation may be significantly less expensive than smoke-free options. As hotel chains increasingly go smoke-free, it is possible that the marketplace will respond with offerings to fulfil consumer demand. As policy makers consider how to regulate P2P services like Airbnb, public health considerations should be included.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".