Bibliographic record
Abstract
Transportation service providers may view travellers with disabilities as a positive market segment for revenue generation, rather than a regulated service requirement. In Canada, a substantial portion (40%) of potential travellers with disabilities has desirable socio-economic and demographic characteristics. A majority of these travellers (70-80%) are being accommodated within the existing accessibility of the transportation system. However, a substantial segment (20-30%) are not being accommodated to their satisfaction within the existing accessibility of the transportation system (based on their experiences while travelling), and could (likely) be induced to travel (or travel more) by improvements in transport accessibility. Based on survey data on long-distance travel behaviour of persons with disabilities, an assumption of additional costs to enhance accessibility, and taking into account demand elasticities for travel across modes of transport, it is reasonable to expect that improved transport accessibility could induce an incremental 630,000 one-way trips by Canadian persons with disabilities, and a further 415,000 one-way trips by their travel companions/accompanying persons without disabilities. This would generate transport revenue gains of $115M (best-estimate) and tourism revenue gains of $710M (best estimate) from Canadian incremental travellers. There could be additional revenue gains from world travellers with disabilities.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".