Enriching Sustainable Transport Decisions: Inputs from Operations Research and the Management Sciences
Bibliographic record
Abstract
Findings from the 2008-2009 Transport Canada project, Methodologies for Identifying and Ranking Sustainable Transport Practices in Urban Regions (Wellar, 2008d) reveal that the research methodologies, methods, and techniques from a number of disciplines apply to the process of making decisions about sustainable transport practices. Evidence in that regard is provided by: 1) the results of keyword-based literature searches; 2) the responses of municipal governments to a survey on the methodologies, methods, and techniques that are used; and 3), the commentaries of experts on the methods and techniques that could be used. The findings are presented in eleven project reports which can be accessed at: http://www.wellarconsulting.com/.This presentation first outlines the major elements and findings of the Transport Canada project. We then suggest how the Operations Research or Operational Research, and Management Sciences (OR/MS) fields could build on that project to enhance the OR/MS contribution to the body of methods and techniques used by municipal governments in making decisions about identifying, adopting, and implementing sustainable transport (ST) practices.The third part of the presentation introduces several OR/MS-based initiatives that we believe could significantly expand the research agenda that has been initiated by the Transport Canada project. Our emphasis in this regard is on drawing attention to what we perceive to be fundamental needs that arise as a result of the empirical lessons learned from the Transport Canada project.
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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 | 0.040 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.038 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.027 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".