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Creating Public Value Through Parasport Events

2017· article· en· W2608609385 on OpenAlexaboutno aff
Gayle McPherson, Laura Misener, David McGillivray, David Legg

Bibliographic record

VenueEvent Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Inclusion (mineral)Public relationsPublic policyGovernment (linguistics)Social changeSociologyPolitical sciencePublic administrationEconomicsSocial scienceEconomic growth

Abstract

fetched live from OpenAlex

The hosting of major events presents an opportunity to shape public policy and potentially enable social change. In this article we discuss two different parasport events, the 2014 Glasgow Commonwealth Games and the 2015 Toronto Pan Am/Parapan American Games, which espoused a philosophy of social inclusion and creating social change in sport for persons with disabilities as an outcome of the events. We contend that, as in wider policies for sport, social inclusion has been more illusory than real, sometimes based on increases in facility usage rather than necessarily developing a broader base of participation. Such outcomes stand in contrast to Bozeman and Johnson's criteria for public value. We argue that the two parasport events were used by policy makers to demonstrate meaningful avenues to social inclusion, social change, and how those in public policy positions have the power to influence and create potential. We examine key policies and policy decision-maker's perspectives, utilizing Bozeman's theory on progressive opportunity, regarding the value of two major parasport events in creating social change for persons with disabilities. We conclude that Bozeman's model of progressive opportunity allows for a more sustainable model for bringing the interests of the market and government agencies together to lead to foreseeable and sustainable social change. Notwithstanding, a clear understanding that policy makers need to realize that structural and societal change will not necessarily happen during the life cycle of Games time.

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)0.005
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0070.012
Scholarly communication0.0120.009
Open science0.0020.017
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.001

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 head0.075
GPT teacher head0.387
Teacher spread0.312 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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