Engaging Multiple Actors in Large-Scale Transport Infrastructure Project Appraisal: An Application of MAMCA to the Case of HS2 High-Speed Rail
Bibliographic record
Abstract
The most widespread approach to transport appraisal is to combine cost-benefit analysis (CBA) with environmental assessments and public consultations. However, large-scale transport projects such as the HS2 high-speed rail system in the UK seem to have pushed this approach beyond its limits, leading to broad discontent with the appraisal process. There is a need both to develop new methods capable of integrating a wide range of perspectives in a systematic manner and to test these for large-scale projects. Multicriteria analysis (MCA) has proven useful in supporting transport decision-making by including a broader set of criteria in the appraisal process. Multiactor multicriteria analysis (MAMCA) has extended this approach to include multiple actors and stakeholders in the judgment and decision-making process. This paper builds on the MAMCA method and demonstrates its practicability and usability by applying it to the case of HS2. The purpose of this paper is not to reach a definitive conclusion on the desirability of various project options, but to complement existing transport appraisal methods by making different perspectives explicit. For example, the results for this case show contrasting views for different groups of transport professionals: a favorable assessment of HS2 among transport planners employed in government, but an unfavorable assessment among transport researchers with a background in sustainability. In terms of contribution to the development of data collection methods, this research demonstrates the usefulness of conducting semistructured interviews in conjunction with an online questionnaire for the assessment and weighting process within MCA. Because MCA results are expressed in terms of relative desirability of projects, the approach also effectively systematizes the inclusion and assessment of multiple options. Overall, the proposed method enhances the capacity to analyze conflicting views in large-scale transport project appraisal processes.
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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 | 0.065 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".