Beyond Sight Specificity: Investigating Third Party Guiding Services for Blind and Partially Sighted Sightseers
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".