The Time Has Come, the Walrus Said, to Talk of Many Things: Wheelchair Securement Spaces on Commercial Airlines
Bibliographic record
Abstract
Air travel poses special problems for people who use wheelchairs either periodically or consistently (wheelchair users). The wheelchair is, to some extent, an extension of the wheelchair user’s bodily autonomy. Personal dignity would be enhanced, and injury and discomfort would be reduced, if a traveling wheelchair user were allowed to remain in his or her own wheelchair for the duration of the flight. Although no law or regulation currently requires that option, groundwork has been laid in both case law and statutes that could lead to such a result. To be sure, safety and cost are paramount issues and must be adequately addressed. Some technological concerns have already been resolved and others are the subject of promising developments. Lobbyist groups are actively campaigning and, as a result, some airlines have shown interest in the proposal. The goal of in-cabin use of personal wheelchairs is achievable, but the process is likely to be incremental. During this period of COVID-19 pandemic-related disruption in the airline industry, both mainline and regional carriers should benefit from the Schumpeterian notion of creative destruction resulting in technical and business innovations. The catalyst needed to move the research and development process along at a faster pace might be a contest with some sort of reward such as has been used to foster other aeronautical innovations.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".