Moving Beyond Evaluation to Transit Project Prioritization: Lessons from the Toronto, Ontario, Canada, Context
Bibliographic record
Abstract
Governments face critical decisions on how to spend taxpayers’ money and must weigh priorities and come to these decisions in a transparent and defensible way. A prioritization tool can play a critical role in informing spending decisions, ensuring that decisions are made in the interest of the public good, and bolstering public confidence in elected officials and the democratic process. Ideally, a prioritization tool not only evaluates potential projects against a desired set of policy objectives but also prioritizes potential projects into an implementation plan through the integration of pragmatic considerations. Metrolinx, an Ontario, Canada, provincial agency tasked with transportation planning for the greater Toronto and Hamilton area, developed a prioritization framework to make recommendations on capital investment in sustainable transportation. This paper summarizes the current prioritization framework, outlines its limitations, and goes on to explore potential remedies to those limitations as well as inherent challenges. Specifically, the paper discusses incorporating broader considerations, including multimodal integration and active transportation, congestion, network effects, and project interdependencies, and bridging the gap between project evaluation and real-world prioritization. The paper presents best-practice research for each broader consideration and posits that these broader considerations can be used to transform evaluation outputs into prioritized implementation plans.
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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".