The Cities vs. AirBnB (Housing Party, ep1)
Bibliographic record
Abstract
Where and when is Airbnb activity happening in Canada? Who is making money on Airbnb and how? And are short-term rentals threatening long-term housing in Canada? You'll find the answers to all this and more on the new bi-monthly show-within-a-show 'Housing Party' from Unpacking the News-! Join housing and human rights doctoral student David DesBaillets (Universit' du Qu'bec ' Montr'al, Project Montreal) as he welcomes critical urbanist David Wachsmuth (McGill School of Urban Planning, Canada Research Chair in Urban Governance) to explore his research on AirBnB and the good and bad ways in which online and digital platforms are distorting and transforming the Canadian housing sector. Join David DesBaillets every two months for a new conversation on housing in Canada with experts, policy wonks and scholars examining the challenges cities face in providing affordable, adequate and accessible housing for Canadians. For more on David Wachsmuth's research go to https://davidwachsmuth.com/ This conversation was recorded on August 9th, 2019.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.198 | 0.031 |
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".