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Record W4413882296 · doi:10.61701/450843.676

The Two Faces of Airbnb Disruption: Licensed Accommodations Perspective on Contrasting Impacts on Community in Nova Scotia

2025· article· en· W4413882296 on OpenAlexaboutno aff
M. Scott Matthews, Ioannis S. Pantelidis, Rodrigo Milano de Lucena

Bibliographic record

VenueICHRIE Research Reports · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaNova (rocket)Perspective (graphical)BusinessGeographyEnvironmental scienceAeronauticsComputer scienceEngineeringArchaeology

Abstract

fetched live from OpenAlex

This paper examines the disruptive impact of Airbnb on urban and rural communities within the province of Nova Scotia, Canada, from a licensed accommodations perspective. Utilizing qualitative methods, this study gathers insights from general managers and owners in the licensed accommodation sector to understand similarities and differences in how Airbnb influences socioeconomic factors, housing markets, and community dynamics in both urban and rural settings. Overall, Airbnb's influence is double-edged, offering economic opportunities while simultaneously posing significant socioeconomic challenges. The findings underscore the need for nuanced policy approaches relating to Airbnb that balance economic growth with community well-being, ensuring sustainable development in both urban and rural contexts.

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 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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.405
Teacher spread0.301 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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