Dimensions of Knowledge Management on Good Urban Governance (Case Study: Municipality of Rasht City, Iran)
Bibliographic record
Abstract
Understanding the environment and the necessity of dealing with issues Arising from the pressures arising from environmental variables, regardless of the issue to gain competitive advantage, which is extremely necessary, decisions and actions will affect managers. Due to the lack of influence of each variable, lead to problems such as pervasive poverty, unemployment, inflation, environmental pollution, destruction of infrastructure, conflict, and other abnormalities in the city. The main purpose of this study, the effect of knowledge on good urban governance in the city of Rasht. The study is a descriptive survey. The study population included all employees of the municipality of Rasht that the number of people was 2191 and the sample sizewas327people. This measurement tool, the researcher made questionnaire. Methods of descriptive statistics and statistical tests are t-test and Pearson correlation. The results of the Pearson correlation test showed the dependent variable have high correlation with independent variables of knowledge of good urban governance. T-test results also showed that the variables knowledge, organizational learning, knowledge transfer, stored knowledge, user knowledge, creation knowledge affect in good urban governance.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".