Challenges and Strategies in Benchmarking Intercity Passenger Rail Performance
Bibliographic record
Abstract
Assessing performance of intercity passenger rail services is relevant for government policy makers, rail infrastructure owners and managers of train operating services. However, assessing performance is no easy task, given that performance is largely a relative concept which requires comparison between different operators. The dynamics and contextual environments of the intercity passenger rail industry further pose a number of challenges to the comparative evaluation of intercity passenger rail operator performance. The authors were part of a team undertaking a study for Transport Canada whose objective was to compare the performance of VIA Rail – Canada’s only intercity passenger rail service – to international intercity passenger rail operators. The study took into account the influence of different governance models and operating environments, and drew out related public policy lessons for VIA Rail. Though the results of the study are confidential, the key challenges in benchmarking intercity passenger rail performance and the strategies used to interpret related performance are presented with the aim of informing similar research in future. The discussion in this paper is specific to intercity passenger railway performance but many related lessons and tools are also applicable to benchmarking performance in other transportation sectors.
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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 | 0.188 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".