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Record W4399728138 · doi:10.32920/26046619

Beyond Sight Specificity: Investigating Third Party Guiding Services for Blind and Partially Sighted Sightseers

2024· preprint· en· W4399728138 on OpenAlexaff
Madeline Smith

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsToronto Metropolitan UniversityProfessional Engineers Ontario
Fundersnot available
KeywordsPartially sightedSightBlindnessPsychologyComputer scienceOptometryMedicineHuman–computer interactionVisually impairedAstronomyPhysics

Abstract

fetched live from OpenAlex

This paper investigates the techniques third-party guides employ to create positive experiences for their blind and partially sighted clients. The literature reviewed outlines the implications of sightseeing while blind and the role that third party guides can play within the experiences of sightseeing, particularly for those who experience disability . Qualitative content analysis was used on five transcripts of expert interviews conducted by the researcher with professional travel agents and tour providers who provide services to blind and partially sighted clients. The results of the analysis indicate the importance of thorough communication among all stakeholders prior to the sightseeing experiences, the need for expert scouting trips to assess the accessibility of prospective destinations, and the role on-site guides play in chaperoning and supporting clients. Despite challenges encountered in their work, the experts prioritise educating tourism professionals and members of the public in an effort to reduce the attitudinal barriers that stand in the way of robust accessibility improvements. 1 Throughout this MRP when discussing disability I employ person-first language, in accordance with Wright's (1960) seminal argument: "Since physique does stimulate value judgments, it is particularly important to use expressions insofar as feasible that separate physical attributes from the total person" (p. 8).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0030.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.284
Teacher spread0.224 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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