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Record W3167597761 · doi:10.7202/1076662ar

Airbnb, le partage du logement et le droit au logement à Montréal

2020· article· fr· W3167597761 on OpenAlex
Danielle Kerrigan, David Wachsmuth

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNouvelles pratiques sociales · 2020
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

La prolifération des locations à court terme, ainsi que les entreprises qui les supportent, ont suscité de nombreux débats houleux dans un nombre croissant de villes concernant l’usage approprié des propriétés résidentielles. Sont-elles des actifs pouvant être convertis à un usage plus rentable en tant que logement touristique, ou s’agit-il de logements pour les résidents locaux ? Cet article analyse le cas de Montréal et constate que les bénéfices financiers des locations à court terme sont fortement concentrés, alors que la ville entière souffre de la conversion de près de 5000 logements locatifs. Nous explorons les tensions entre l’écosystème croissant des entreprises qui facilitent la professionnalisation des hôtes ainsi que les résidents et groupes communautaires de Montréal qui luttent pour leur droit au logement. Nous concluons en discutant des mesures réglementaires qui permettraient de détourner le marché des locations à court terme des opérations commerciales pour le diriger vers un réel partage de propriété résidentielle.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.040
GPT teacher head0.233
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it