Profit Model of Metro Enterprises and Quasi-Market Based Practice of Shenzhen Metro
Bibliographic record
Abstract
Abstract The need to financially support metro enterprises stems from the amplified global enthusiasm for sustainable modes of transport. This paper analyzes the formulation and practice of the profit model based on quasi-marketing initiated within Shenzhen Metro in China. In contrast to previous studies based on a single theory, this paper employs an integrated approach in optimizing gains. Metro enterprises have peculiar attributes such as supply of quasi-public service products, positive externalities, heavy assets, low profit, economy of scale, and economy of scope. Therefore, in order to effectuate sustainable growth, the author has put forward three methods for the profit model based on the quasi-market principle: firstly, generate internal gains of positive externalities through the enterprise’s market operation on government-allocated resources; secondly, balance cost and income by modifying the accounting policies on fixed assets depreciation and financing interest; and lastly, maximize economy of scope by enhancing synergy between different business segments and sub-businesses of the same segment in the enterprise. In practice, these methods are carried out in Shenzhen Metro with innovative methods that comprise “metro plus property”, “member plus fund” and “entity plus virtual”. This study concludes that the advantages of quasi-marketing include the optimization of resources and the success in overcoming the financial restraints in metro enterprises. Through the high applicability in Shenzhen Metro, it is shown that this quasi-market principle-based profit model could enable metro enterprises to achieve self-development and sustainability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".