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Record W2907730641 · doi:10.14288/1.0372362

Lobbying in British Columbia's designated resort municipalities : the case of short term rentals

2018· article· en· W2907730641 on OpenAlexaboutno aff
Helena Riggs Konanz

Bibliographic record

VenuecIRcle (University of British Columbia) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsTerm (time)RentingBusinessHistoryPolitical scienceLaw

Abstract

fetched live from OpenAlex

This thesis focuses on the subject of short-term rentals and their host companies, such as Airbnb, to study the influence of business interests at the local level of government in British Columbia. Worldwide, these companies and their listings have grown exponentially in the last five years, but not without controversy, as they have met up against tenant rights groups, municipalities, and hotel associations who are upset about its effects on housing, neighborhoods and the traditional accommodation sector. Government regulations and municipal policy making are examined to understand the power of lobbying in this context. The lobbying efforts of both Airbnb and the hotel/motel associations are investigated and analyzed, from data collected from both interviews and a survey of local officials in British Columbia’s fourteen designated resort municipalities. The findings confirm that lobbying in municipalities is very active, but the practices are distinct from other levels of government. Recommendations include a lobbyist registrar at the local level to foster transparency and accountability, and a new way of looking at how all levels of government ought to approach regulating the new normal of online platform industries.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.325
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.190
Teacher spread0.169 · 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
Published2018
Admission routes1
Has abstractyes

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