Bibliographic record
Abstract
Future direction in barrier-free transportation is being charted by users sharing their travel experiences. The Taking Charge of Air Travel Survey is a Canadian best practice with international applicability. The central objective of the survey is to gather reliable statistical data on the accessibility of air travel in Canada for seniors and persons with disabilities. This information will be used to help the Canadian Transportation Agency (the Agency) evaluate the progress that has been made to remove obstacles to air travel and to set goals and priorities for improving access to air transportation. The results of the survey are expected to also be used by air carriers and airports to learn more about the experiences and concerns of travelers with disabilities and to gain insight into how to make their operations more accessible. In the summer of 2000 a real-time snapshot of transportation accessibility was taken. Seniors and persons with disabilities were approached by Agency field-staff at Canadian airports and asked to fill out a questionnaire which examined whether or not they feel they are being given access to the kind of service they require to prepare for and take flights with a minimum of obstacles. The survey dealt with issues such as: booking agents awareness of services and facilities for seniors and persons with disabilities; ease of access to airports; readability of flight schedule monitors; clarity of public address announcements; helpfulness of check-in personnel; facilitation of passenger boarding and baggage handling; seating and washroom arrangements; availability of wheelchairs on aircraft; and accommodations provided for service animals. The paper will present the findings of this Taking Charge of Air Travel User Satisfaction Survey. Policy implications of the findings will be highlighted. The application of these findings in future regulatory development and service provision will be discussed. The survey methodology will be presented to support the validity of the findings. A direct benefit of this survey methodology is giving consumers a hands-on opportunity to tell program planners and service providers how to best improve safety, security and independence for travelers with disabilities. The paper will illustrate how this participatory approach has applications in other countries around the world. Customer satisfaction is the yardstick of program effectiveness measured by this practical results-oriented methodology.
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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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".