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Record W1519845614

The Social Economy and a Special Event: Community Involvement in the Whitehorse 2007 Canada Winter Games

2009· article· en· W1519845614 on OpenAlexaffabout
Margaret Johnston, G. David Twynam

Bibliographic record

VenueNorthern review · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsThompson Rivers UniversityLakehead University
Fundersnot available
KeywordsAttendanceEvent (particle physics)Context (archaeology)SociologyPublic relationsSocial psychologyPolitical sciencePsychologyEconomic growthHistoryEconomics
DOInot available

Abstract

fetched live from OpenAlex

This article is premised on the idea that our understanding of the social economy can be developed through an examination of community engagement in a special sporting event. It explores the extent to which hosting a special event in Whitehorse, Yukon provided involvement opportunities for community members, and it explores the related outcomes for individuals and the community. The particular nature and attributes of Whitehorse as a northern community and the nature of the event-the Canada Winter Games-set the context for how the community and its members engaged with the event. The research explored involvement with the event in order to come to an understanding of specific outcomes in Whitehorse, and in relation to special events in the social economy more generally. Findings indicate increasingly positive assessments of the Games' impacts and resident involvement in the event through support, attendance, and volunteering. The highest-ranked motivations for volunteering suggest a strong connection to the event, linked in with the opportunity to contribute to the wider community goals of hosting the event. The findings of the study reiterate the importance of considering the local context of the social economy when exploring its expression through a special event. The basis for this article is a longitudinal research project that includes surveys, focus groups, and interviews in the community, with an emphasis on event volunteers.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.311
Teacher spread0.281 · 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 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

Citations0
Published2009
Admission routes2
Has abstractyes

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