Bibliographic record
Abstract
In recent years, the number and breadth of the Canadian Transportation Agency’s accessibility standards have increased and many provisions contained in the standards have become more complex. As there are more standards, the number of transportation service providers affected by them has also grown. As a result, monitoring Canada’s federal transportation industry for compliance has become increasingly challenging. In 2008, an Agency review determined that its monitoring program needed to focus more on enhancing compliance within the transportation industry and less on gathering and reporting data. The Agency developed a new, risk-based monitoring framework that sets specific monitoring objectives, lays out guiding principles for fairness, transparency and flexibility, and establishes criteria to set monitoring priorities. This framework promotes a new approach that targets specific areas of higher risk for non-compliance and is proactive in helping the transportation industry to comply with the standards. Because the new approach has specific targets, it is easier to administer, allows for more frequent and timelier reporting of results, and provides more transparent and concrete information to the transportation industry and persons with disabilities. This paper will compare the Agency’s new monitoring approach with its former approach and discuss the merits of a targeted, collaborative monitoring system that focuses on higher-risk areas.
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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 | 0.227 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".