Outcomes of the AAMVA/CCMTA Forum--Challenging Myths and Opening Minds: Aging and the Medically At-Risk Driver
Bibliographic record
Abstract
The American Association of Motor Vehicle Administrators (AAMVA), in conjunction with the Canadian Council of Motor Transport Administrators (CCMTA), developed a forum entitled Challenging Myths and Opening Minds: Aging and the Medically At-Risk Driver to bring together licensing administrators, medical professionals, researchers and nongovernmental groups representing the aged. The purpose of the workshop was to review the issue of aging and medically at-risk drivers from many different perspectives. General agreement was reached on a number of concepts that were felt to be important and these concepts were then incorporated into a list of core values. Safety has to be the primary concern in any programme dealing with driver licensing and age should not, in itself, be a determining factor in licensing decisions. Practical, comprehensive medical standards should alleviate any requirement for age-based standards. Medical standards must be evidenced-based, practical, flexible and acceptable to the public. Standards should be re-evaluated at frequent intervals.
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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".