Exploring to Service Innovations in Shanghai Metro System: Based on the Model of MFCSI
Bibliographic record
Abstract
This paper studies service innovations that have been developed in Shanghai Metro System within last seven years. The information about service innovations are mainly gathered from Shanghai Metro’s official website concentrating on the model of five levels of classification of service innovation in enterprises (MFCSI)which was developed by a group of scholars in Donghua University, China. The survey on passengers and interviews on the operation and managerial staffs are also carried out related with their services and innovations. Data collection was done via Websites-investigation, field observation and interview in five metro stations in Shanghai. Totally 18 service innovation cases are analyzed by the Model of MFCSI. Meanwhile, some solutions for improving were found for better development of MFCSI as a new measuring tool concerning the increasing service firms’ competence, which is similar to the new points in the service science field with the potential to be used more further in services firms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".