‘All Ages and Abilities’: exploring the language of municipal cycling policies
Bibliographic record
Abstract
As cities work to support greater uptake and equity in cycling, the terminology ‘All Ages and Abilities’ (or AAA) is increasingly common in cycling research and practice vernacular. However, it is unclear the values that underlie this. We undertook a policy scan of Canadian municipal and regional policy documents to understand: the language used to describe ‘All Ages and Abilities’; the infrastructure specified; how municipalities and regions define a cycling network; and how equity and priority populations are incorporated into these plans. Of 35 plans, 25 mentioned ‘All Ages and Abilities’. Fourteen mentioned specific ‘All Ages and Abilities’ infrastructure, with cycle tracks, local street bikeways, and multi-use paths most frequent. Reference to the idea of a network was common (32 plans), with some defining this as a minimum grid. Within plans that used ‘All Ages and Abilities’ language, children and older adults were the most common populations mentioned (e.g. ‘Ages’), but there was more ambiguity around who was being referred to with ‘Abilities’. As use of this terminology continues, clarity is needed on the meaning and values that underpin it. A lack of specificity in design standards and whom this infrastructure serves is a barrier to concrete, consistent implementation.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".