The Role and Place of Cities in the Knowledge-Based Society and Economy (A Case Study of Iran)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».