The Role and Place of Cities in the Knowledge-Based Society and Economy (A Case Study of Iran)
Bibliographic record
Abstract
This article considering the interrelationships between knowledge and Urban Development. An Overview is of complementary views, such as knowledge-based cities, learning cities, intelligent cities and creative cities. There are four main channels whereby cities join the knowledge society: human capital, economic structure, innovation systems, information and communication technology. On this basis provinces of Iran are subjected to a comparative analysis of their knowledge indicators in each of these four components. The study is descriptive-analytical and practical in terms of objective. For comparative analysis, 31 provinces of Iran and 55 indexes were selected based on knowledge. Use was made of Shannon Entropy to measure relative importance and weight of each index. Also, usingmulti-attribute decision making methods of TOPSIS and VIKOR to rank provinces and cluster analysis was used to classify. Finally, using the software Arc GIS, map of the provinces benefit levels were drawn. Research findings showed that using TOPSIS method the benefit rate of provinces in Iran from the indexes of knowledge-based society and economy and rank of each province in different indexes represented a high loss of imbalance in the how distribution of these indexes in the provinces of the country. According to this, Tehran province was considered as the highest benefit province and is ranked 1on all four indexes of human capital, economic structure, innovation and ICT systems with values (priority factor) 0.941, 0.741, 0.8206 and 0.752, respectively. In contrast, provinces of South Khorasan in “human capital” index with value 0.009, Ilam in “economic structure” index with value 0.140, Kohgiloyeh-Buyerahmad in “innovation systems” index with value 0.024, and North Khorasan in “information and communication technology” index with value 0.062 were identified as the lowest benefited provinces. Using TOPSIS and VIKOR methods, assessment of the province’s position in knowledge-based society and economy suggests that in the TOPSIS method, Tehran was recognized as the highest benefit province with value 0.740 and Ilam was considered as most deprived benefit province with value 0.069. But in the VIKOR method, Isfahan was identified as highest benefit province with value 1.129 while Ilam was determined as the most deprived benefit province with value 0.627. According to the scores obtained from the above methods, through density-based hierarchical cluster analysis method, the country provinces have been classified into 3 equal groups. On this basis, Tehran province was at the highest benefit level in cluster 1, Isfahan, Khorasan Razavi, East Azerbaijan, Fars and Khuzestan were at the half benefit levels in cluster 2 and the other 25 provinces were at the lowest levels of knowledge-based society and economy in cluster 3. Results of classification of provinces showed that the country provinces were located in the heterogeneous and unbalanced conditions. The overall results showed that not a significant proportion of Iranian provinces have been introduced in the process of joining knowledge-based society and economy.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".