Bibliographic record
Abstract
Amusement attraction participation eligibility requirements may be expressed in terms of age, height, weight, health condition, (dis)ability, and combinations of these, and potentially other criteria. Restrictive eligibility criteria can protect manufacturer and operator from liability for injury, since no guest could sustain injury without exposure to the ride. However, new forms of liability have emerged, through litigation under general human rights codes or specific regulations of the Americans with Disabilities Act. This paper describes and compares two approaches to more specifically determining participation eligibility for guests with disabilities and health conditions: the medical approach and the human factors engineering approach. Although many conditions are manifest in a range of severities, the medical approach will either impose the most restrictive eligibility on all guests with the same disability type, or require park personnel to make diagnostic determinations outside their expertise, and requires compilation of data about every disability and condition. The human factors engineering approach identifies functions to be performed by the guest to fulfill the ride experience. Clear description of functions and risks will enable guests to determine whether they are able to safely ride, in consultation with their own advisors. The approach to determining eligibility affects not only whether a particular guest can ride, but also operational practices that affect guests’ relationship with the park
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 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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".