Not all hubs are created equal: An analysis of future mobility hubs in the Greater Toronto Area
Bibliographic record
Abstract
The Problem:The Greater Toronto Area is Canada’s largest metropolitan region, and is home to 5.6million people. The resulting polycentricity of the GTA has diversified commutingpatterns beyond the scope of the existing public transit network, and has contributed tocongestion, homogenous land use, and urban sprawl. In response, a series of mobilityhubs have been created by Metrolinx, Toronto’s regional transit agency, in the hopes ofbetter connecting the GTA through public transit. The goal of this study is to isolatefactors influencing transit use at trip origins and destinations, and determine howchanging neighborhood characteristics can influence the success of a mobility hub, andfacilitate a more connected transit network. We select variables based on previous research, which finds that low income and recent immigrant groups rely heavily on public transit. To improve inequities in existing service,research suggests that transit agencies increase service in underserved vulnerablecommunities; making transit an accessible option for more commuters. Existing research also finds that high frequency transit, land use mixture, employmentopportunities and high density are most conducive to public transit use. [...]
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".