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Record W4242208693 · doi:10.22215/etd/2021-14479

Barriers to Accessibility for Skiing

2021· dissertation· en· W4242208693 on OpenAlexaboutno aff
Lindsay McCauley

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)StorytellingParticipatory designField (mathematics)Citizen journalismEngineeringKnowledge managementPsychologyComputer scienceWorld Wide WebSocial psychologyNarrative

Abstract

fetched live from OpenAlex

Across Canada, ski areas do not consistently or thoroughly recognize the experiences of persons with disabilities. Consequently, barriers to accessibility occur and impede inclusion for skiing. Five types of barriers to accessibility are experienced for skiing: architectural and physical, attitudinal, organizational and systemic, informational and communicational, and technological barriers. This thesis explores how design research can be used to identify and address barriers to accessibility for skiing. Three research phases are conducted: a preliminary field study, a questionnaire, and directed storytelling. A participatory design approach facilitates the researcher, subject matter experts, and target users of adaptive skiing to contribute to design recommendations and practical approaches that may improve the experience of skiing. Lastly, the thesis proposes further research and design of an interactive accessibility map as a novel method for information provision to reduce the impact of barriers to accessibility for skiing.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.449
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.014
GPT teacher head0.354
Teacher spread0.341 · 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
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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Same topicWinter Sports Injuries and PerformanceFrench-language works237,207