Analysis of the Performance of Municipalities in Terms of Urban Good Governance in Shahrekords
Bibliographic record
Abstract
Nowadays, increasing cities population and difficulties due to the centralized programming approach that leading to the disorderliness in cities at one side and awareness and increasing citizens rights at the other side; necessitate the reexamining in urban management and city management. due to the radical changes in cities’ developmental management, in addition to the mentioned approach, we need to deploy a decentralized approach in urban organization or apply a good urban governance with citizens cooperation simultaneously, so for removing current problems in urban development we should take into account the local organizations and governmental and non-governmental organization. The goal of this research is to consider Shahrekord performance in the framework of good urban governance. The research method is descriptive-analytical method, and data and information for this research gathered through field study and used measuring method, and subjects include sample of 330 shahrekod citizens. In this research we applied four standards for good urban governance i.e cooperation, efficiency, transparency and accountability, and for data processing and analyzing we used SPSS software. Findings show that efficiency has a mean equals to 2.95, cooperation 2.67, accountability with a satisfying mean equals to 2.59, and transparency is 2.58. so shahrekords’ municipal performance in the framework of good performance is unsuitable, so research hypothesis which imply on suitable municipal performance at the framework of good urban management is unsatisfying and not being proven.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".