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Work hard, play hard: selling Kelowna, BC, as year‐round playground

2005· article· en· W2020771724 on OpenAlexaffvenueabout
Luis Aguiar, Patricia Tomic, Ricardo Trumper

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsOkanagan CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsFrontierRestructuringClearanceWork (physics)Political scienceSociologyEngineeringLaw

Abstract

fetched live from OpenAlex

A keen interest in place making and place selling is widespread in contemporary society. While the bulk of academic research has focused on studying the restructuring of large urban conglomerates, places beyond the exploding metropolis, by comparison, have received little attention, especially when it concerns Canadian landscapes. In an attempt to study the particularities of place making in contemporary smaller, more isolated communities—hinterlands—this work analyses the city of Kelowna, in British Columbia, Canada. We argue that historically Kelowna, a small rural community specialising in ranching, forestry and fruit production, since the early 1980s, has been re‐imagined and re‐designed, on the one hand as an all‐year playground and as an innovative frontier for high‐tech industries; on the other hand, this post‐Fordist reinvention contains a discourse of ‘whiteness’, one that entices by packaging ‘place’ in terms of ‘sameness’ and ‘familiarity’. In contrast to large cosmopolitan post‐industrial cities, hinterland‐type cities are invented, sought and lived as geographies cleared from the ‘elements’ that make cities ‘unsafe’.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.007
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.226
Teacher spread0.205 · 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.

Study designNot applicable
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

Citations43
Published2005
Admission routes3
Has abstractyes

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