Bibliographic record
Abstract
Transportation planning is in the midst of a paradigm shift: a change in the way transportation problems are defined and solutions evaluated. The old paradigm assumed that “transportation” means automobile travel, so transportation planning consisted of accommodating more and faster motor vehicle traffic. The new paradigm recognises a wider range of options, including non-motorised modes, and a wider range of planning objectives. The new paradigm does not assume that more travel is necessarily better, but instead strives for “optimality”—that is, a proper balance, so each mode is used for what it does best. With better planning, we can create a healthier, more efficient and more equitable transportation system. A key step in this paradigm shift is to recognise the full value of non-motorised modes (walking, cycling and variants such as wheelchairs and scooters). Non-motorised travel is basic and essential. It is virtually universal, used by almost everybody, both alone and in conjunction with other modes. For example, bus and train stations, ferry terminals, airports and parking lots are pedestrian environments, and most motorised trips involve walking links, such as walking or cycling to a bus stop, or walking from a parked car to destinations. …
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.037 | 0.019 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".