A multiple criteria decision process : the case study of rapid transit in Greater Vancouver
Bibliographic record
Abstract
One of the most fundamental issues in the field of planning revolves around evaluation within the decision making process. What determines how governments, or government agencies, allocate public resources? What social, economic, environmental, and political considerations are taken into account? Who is involved in this process? The purpose of this thesis is to examine evaluation methodology using the case study of the decision on the next phase of rapid transit within the Greater Vancouver area. The thesis begins by reviewing the major evaluation methodologies available for the decision: Cost-Benefit Analysis, Planning Balance Sheet, Goals Achievement Matrix, Multiple Criteria Analysis, Multiple Accounts, and the Delphi Process. An analysis of each model's strengths, weaknesses, and historical applications shows that no one is adequate for our decision. The emphasis of evaluation is typically to produce an answer for decision makers. The purpose of the literature review is to see what opportunities exist within these various models to include a process for decision making. The emphasis is not on identifying the best route, but how to decide which would be the best route. The focus is on what factors should be accounted for, and who should be involved in the process. Rapid Transit options have been reviewed and evaluated by two government agencies, the Greater Vancouver Regional District (GVRD) and BC Transit, with conflicting results. The GVRD's evaluation used a quasi-Multiple Accounts, and the BC Transit review was a combination of Multiple Accounts and Multiple Criteria Analysis. The two studies, like the theoretical models they are based on, suffered practical shortcomings. Prominent in both was a lack of public participation in the7 final decision-making process. After reviewing the various methodologies and practical problems with the case study, the thesis offers a model based on simple Multiple Criteria Analysis, Multiple Accounts, and the Delphi Process. The hybrid model is sufficiently comprehensive to account for all of the relevant economic, social, and environmental factors and sufficiently robust to include public input to the decision making process. The aim of the thesis is not to radically change the scope of evaluation methodology, but to set it in a broader socioeconomic context.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".