MétaCan
Menu
Back to cohort
Record W1998474565 · doi:10.3727/154427207785908092

Motives, Rural Images, and Tourism Brokering Roles of Rural Accommodation Entrepreneurs in South Western Ontario, Canada

2007· article· en· W1998474565 on OpenAlexaboutno aff
Barbara A. Carmichael, Kelley A. McClinchey

Bibliographic record

VenueTourism Review International · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsAccommodationTourismRural tourismRural areaProduct (mathematics)EntrepreneurshipConsumption (sociology)Quality (philosophy)Tourism geographyGeographyBusinessEconomic growthSociologyPolitical sciencePsychologySocial science

Abstract

fetched live from OpenAlex

This study focuses on the rural images of rural accommodation operators, their motivations in terms of "lifestyle" aspirations, and their "brokering" role in the understanding of tourists' needs and experiences in rural areas. We know little about the rural image perceptions of tourism entrepreneurs, the importance that they place on a rural lifestyle, and how they influence their guests' experiences of rural life. Similarly, little is known about knowledge transfer between hosts and their guests. The study area for this research is in a rural region of small towns and villages in South Western Ontario, Canada. Through personal interviews, rural entrepreneurs showed a strong interaction with tourists, a sound knowledge of tourist motivations, and where tourists go in the region. They performed a role of validating and recommending tourist activities and thus influencing consumption patterns. Their role as tourism "brokers" is important because as well as recommending the tourism product, they can get feedback on the quality of tourist experiences in the region. This article supports the notion of lifestyle entrepreneurship as the norm in rural tourism, at least in this region, and emphasizes the importance of the rural landscape to quality of life for both hosts and guests.

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)0.000
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 score0.145
Threshold uncertainty score0.793

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueTourism Review InternationalSame topicWine Industry and TourismFrench-language works237,207