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Record W2604104791 · doi:10.1108/ijchm-10-2015-0530

Enhancing destination competitiveness through disability sport event legacies

2017· article· en· W2604104791 on OpenAlexaff
Tracey J. Dickson, Laura Misener, Simon Darcy

Bibliographic record

VenueInternational Journal of Contemporary Hospitality Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsTypologyTourismPublic relationsContext (archaeology)DestinationsSport managementMarketingSociologyBusinessPolitical scienceGeography

Abstract

fetched live from OpenAlex

Purpose This paper aims to contribute to the event legacy discourse by exploring the nexus between event legacy literature and destination competitiveness by focusing on disability sport events or parasport and addressing an identified gap in the research literature. Design/methodology/approach This is achieved through conducting a systematic review of disability sport events literature; performing an audit of international disability sport events; developing a typology of disability sport events; and outlining a research agenda drawing upon these previous steps. The typology is then placed in context to the destination competitiveness framework to provide direction for both host organizing committees and tourism destination managers. The research framework reflects the complexity of disability sport events with specific reference to the social impact of disability sport events for destinations. Findings Despite calls for increased research into accessible tourism and events, the potential social legacy for communities and destinations from disability sport or parasport events remains absent from most sport, event and tourism literature. The findings and resultant typology from this study provide an interdisciplinary approach to value add to the disability sport event and destination management sectors. The combined understanding of both sectors creates an opportunity to leverage further events through marketing accessibility as a competitive advantage, seizing opportunity for international and national disability events, and the subsequent event accessible tourism and general accessible tourism that improved destination accessibility provides a host city or precinct. Research limitations/implications The disability sport event typology and a research agenda that supports future research are outcomes of this research. Practical implications These insights are beneficial to both researchers and practitioners interested in leveraging the opportunities from disability sport events to support sustainable destination development and competiveness that reflect the needs of a population with diverse access needs, including our ageing population, those with temporary disability and parents with young children. Originality/value This research lays the groundwork to support the desired social legacy for future host communities. From a theoretical perspective, given the paucity of research on disability sport, the typology offers a means to evaluate and monitor the impacts of various types of events from the perspectives of sustainable development, tourism, accessibility, community engagement and public policy. The addition of understanding destination competitiveness and the underlying criteria for accessible destination development provides opportunities to further leverage disability sport event beyond the event itself for ongoing accessible events, tourism and disability employment opportunities.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0040.005
Scholarly communication0.0160.009
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0200.001

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 head0.045
GPT teacher head0.380
Teacher spread0.335 · 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 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

Citations51
Published2017
Admission routes1
Has abstractyes

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