EVALUATION METHODS OF REGIONAL TRANSPORT SYSTEMS PERFORMANCE EFFICIENCY
Bibliographic record
Abstract
The article considers the problems of the evaluation of the regional transport systems (RTSs) performance efficiency. A technique for its evaluation based on the Data Envelopment Analysis has been provided. The authors proposed an approach to the separation of similar RTSs for the comparative evaluation of their effectiveness. The cluster analysis and the data envelopment analysis were the main methods for the processing of statistical data on the activities and effectiveness of RTSs. An approach to the formation of a set of input and output parameters for RTS analysis and the principle of separation of RTS clusters in Russia have been provided, the methods of the evaluation of RTS functioning efficiency have been developed, which provide for the comparison of RTSs in the context of subjects and sub-sectors. The methods were tested on the basis of Russian RTSs: 5 RTS clusters have been distinguished, on the example of one of which the calculation of RTS efficiency has been shown; upon the results of the study, the authors emphasized the problems of collecting and processing of statistical data distributed by the Federal State Statistics Service of Russia, and also pointed out the principal features of the methods proposed to adapt flexibly to the challenges and the basic data of the study.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".