Ranking the Obstacles of the Establishment of Integrated Urban Management in Tehran Municipality Using MCDM Technique
Bibliographic record
Abstract
Tehran municipality is a nongovernmental organization to which many responsibilities have been added during the years; but the structure and the processes which are predominant in Tehran municipalities and the municipality of other cities is the same basic structure to which some more complicated processes have been appended. Integrated urban management is a solution which has been adopted in many developed countries to solve the problems of municipalities and has decreases the interference of municipality responsibilities with other governmental organizations. It is such a long time that Tehran municipality has started studying the establishment of integrated urban management and published different papers on this field. Anyway, there are some obstacles in the establishment of integrated urban management and it cannot be realized without fully knowing them and programming on their elimination. The present paper first recognizes the obstacles of establishment of integrated urban management and then ranks them through using MCDM techniques. 19 factors have been recognized as the obstacles of the establishment of integrated urban management and then ranked in three different categories titled legal obstacles, environmental obstacles and executive obstacles. The data have been collected using TOPSIS technique and a ranking of above mentioned factors was finally presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".