A Hybrid Approach Based on SERVQUAL, SERVPERF, and IPA for Measuring Transit Service Quality
Bibliographic record
Abstract
The purpose of this chapter is to discuss the public perception of the quality of service in the public transit system in Montreal using a combination of analyses and surveys. The results are used to make recommendations to improve the STM and its perception. General guidelines of SERVQUAL with some additional questions that are more specific to the current social environment of the city are presented. A survey was conducted by asking 250 international graduate Concordia students to rate a series of statements based on the importance of the issue and how much they agreed with the statement, the results were analyzed using three methods: SERVQUAL, SERVPERF, and IPA. The improvement of timetable synchronization between different metro lines and buses is crucial, as well as the education of STM employees in terms of dealing with different ethnicities, languages, and backgrounds are found. The chapter is a rare outside look at the STM and how users perceive the quality of the service, as opposed to the usual internal studies done by the organization itself.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".