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Launching off but Falling Fast: Experiences of Becoming More Physically Active in Response to the Vancouver 2010 Olympic Winter Games

2016· article· en· W2530365823 on OpenAlexaboutno aff
Luke R. Potwarka, Halyna Tepylo, Darla Fortune, Heather Mair

Bibliographic record

VenueEvent Management · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsAudience measurementThematic analysisFalling (accident)EliteAthletesQualitative researchSociologyPsychologyAdvertisingPublic relationsPolitical sciencePoliticsBusinessSocial scienceMedicine

Abstract

fetched live from OpenAlex

Using a qualitative approach, we sought insights into the nature of the relationship between a megasport event and increased activity levels in a sample of young adults living within a host nation. To achieve this purpose, we conducted semistructured interviews with current (and recently graduated) university students living in Canada who indicated becoming more active as a result of the Vancouver 2010 Olympic Winter Games. Through our thematic analysis, we identified three interrelated and overarching themes: connecting through engaged viewership; harnessing the connection; and launching off but falling fast. Connecting through engaged viewership refers to personal and meaningful connections participants made with the Olympic athletes they watched. The act of watching elite athletes compete appeared to inspire participants to make positive activity-related changes in their own lives. Harnessing the connection refers to supportive social and built environments, as well as access to particular resources that enabled participants to act on their inspired state. Launching off but falling fast refers to the intense, but fleeting, nature of changes to participants' activity levels. We conclude the article by discussing the significance of our findings in terms of research and practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
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 head0.018
GPT teacher head0.306
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations12
Published2016
Admission routes1
Has abstractyes

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