Evaluating the Performance of Municipality in Terms of Good Urban Governance (Case Study: District 1 and 3 of Zahedan City)
Bibliographic record
Abstract
Good urban governance is one of the aspects of urban management that has recently caught the attention of western countries and societies. In fact, there are no other options for management and administration of cities except for paving the way for the development of democracy. In this regard, a new form of governance called good urban governance has been found. Therefore, the objective of the present study was to evaluate the performance of urban management using the approach of good urban governance in Zahedan city. To achieve this, the present study was conducted using analytical-descriptive and field-survey methods. Furthermore, the study population of this research included district 1 and 2 residents of Zahedan city. 200 of them were selected using Cochran’s method and questionnaires were distributed among them using simple random method. In order to evaluate urban management in the studied regions, five good urban governance indicators including transparency, participation, accountability, lawfulness, and effectiveness were used. The results of Mann-Whitney test indicated the better condition of district 1 in two aspects of accountability and lawfulness compared to district 3. In addition, t-test results showed that the aspect of effectiveness has the best condition with a mean of 3.21 and the aspect of participation has an unfavorable condition compared to other aspects with a mean of 2.58.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".