The Big Move: Transforming Transportation in the Greater Toronto and Hamilton Area - Modelling and Measuring Against the Triple Bottom Line
Bibliographic record
Abstract
In November 2008, Metrolinx - the transportation authority for the Greater Toronto and Hamilton Area - released its 25-year regional transportation plan: The Big Move. The Big Move adopts the bottom of a high quality of life, a protected environment and a prosperous economy as its foundational basis. The triple bottom line is used to inform the development and measurement of a comprehensive vision, goals and objectives for the regional transportation system, and to model and analyse system performance and implementation options. This paper describes the process followed by Metrolinx to develop the goals and objectives for the regional transportation plan and the metrics that will be used to measure its implementation. The paper provides an in depth overview of the technical modelling and system performance analysis undertaken to support the development of the plan. The paper describes the way in which an iterative process of stakeholder input and technical analysis were used together to select a recommended regional rapid transit network. Finally, the paper provides an overview of the Benefits Case Analysis that Metrolinx is using to select and prioritize project implementation options based on the triple bottom line.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".