Bibliographic record
Abstract
Abstract Returning to the issue of “making transportation systems accessible “ to the disabled-how should that be done? As policy problems go, this one is relatively small in scale. It presupposes that we have decided, in some more general way, that the disabled deserve special governmental measures designed to benefit them. In a scheme of statutory provisions that aim to do so, those that regulate access to transportation play but a small part. Such statutes generally also define “disability, “ indicate who is authorized to determine whether someone is disabled, set out remedies against discrimination, lay down requirements on employers and schools, and much more. The question of how to make transportation systems accessible to the disabled arises against this sort of backdrop. It has arisen in many different jurisdictions of all different sizes. In the 1980s, the city of Munich worked out an innovative timetable information system for its public transport with an eye to advising the disabled how to minimize awkward transfers.1 In 1992, Canada mandated that its national transportation system be made accessible to the disabled.2 The Swedish government, which early mandated uniform accessibility standards for common carriers, was apparently one of the first governments to be concerned with how easily the disabled could switch from one mode of transport to another on a long-distance journey.3 The European Union, for its part, has developed hortatory guidelines aimed at promoting “flexibility “ and “seamlessness “ in the travel of the disabled.4 As a relatively small-scale policy question, this one about making transportation systems accessible to the disabled is the sort of issue that is typically handed over to administrators. As a question that is now beginning to be addressed all over the world, it is not one of merely parochial interest. Drawing on an insightful case study by Robert A. Katzmann, I will use the issue to illustrate the importantly different modes of reasoning available to us as we make such policy.5
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 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".