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Record W2558890445 · doi:10.1080/02642069.2016.1255728

Inclusive by design: transformative services and sport-event accessibility

2016· article· en· W2558890445 on OpenAlexaboutno aff
Tracey J. Dickson, Simon Darcy, Raechel Johns, Caitlin Pentifallo

Bibliographic record

VenueService Industries Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsTourismContext (archaeology)Public relationsTransformative learningService (business)Convention on the Rights of Persons with DisabilitiesPolitical scienceBusinessMarketingSociologyHuman rightsGeographyLaw

Abstract

fetched live from OpenAlex

This paper examines the service dimensions required to be inclusive of people with access needs within a major-sport event context. The United Nations Convention on the Rights of Persons with Disabilities seeks to counter disability discrimination and enable citizenship rights of people with disabilities, including access to goods and services, across all dimensions of social participation including major-sport events (e.g. Olympic and Paralympic Games, world cups in football, cricket and rugby union). Providing for people with disability and access needs is also an emerging tourism focus with initiatives addressing accessible tourism included in the World Tourism Organizations mission and recent strategic destination plans. To enhance the understanding of service delivery for an accessible tourism market in a major-sport event context, a case study of the Vancouver Fan Zone for the FIFA Womens World Cup Canada, 2015 TM is analyzed through the lens of transformative services. From this analysis future research directions are identified to benefit those with access needs who wish to participate in major-sport events.

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)0.008
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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0060.019
Scholarly communication0.0130.006
Open science0.0010.012
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.001

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 head0.037
GPT teacher head0.331
Teacher spread0.295 · 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

Citations91
Published2016
Admission routes1
Has abstractyes

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