Bibliographic record
Abstract
The aging of the world population, and call for greater equality in access to public environments has led to an increase in design for persons with reduced mobility (PRM). There are numerous physical and operational constraints and parameters to overcome when designing a successful and marketable PRM environment. Each program evaluates what is to be considered reasonable based on these guidelines (cost, weight, manufacturability, airframe curvature, footprint required, regulations, and usability). However, there are other less tangible parameters to address. For example, what level of dignity or level of privacy does the PRM environment allow? Does the design require additional assistance to access, or can those who are able make independent use of the environment? Most aircraft manufacturers and design entities have recognized the need to improve accessibility aboard single aisle commercial aircraft (Airbus 320 family, Boeing 737, Embraer 190, Bombardier CSERIES). Current efforts are a step in the right direction, but significant effort and continued improvement is required to accommodate the mobility impaired segment of our travelling public. There is a preconception that design for mobility impairments is costly, takes up a lot of valuable space, and is considerable effort for a relatively small segment of the travelling public. In reality, there are real benefits to be realized for providing environments able to accommodate a greater percentage of the population. A significant competitive advantage is likely to result from design for PRM accessibility. Operator choosing to consider this growing segment of the population will benefit, as news (both positive and negative) travels extremely fast in social media circles.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".