Mobile Communications, the Internet and the Digital Economy: Comparisons and Lessons from Four Major Developing Countries - China, India, Mexico and Brazil
Notice bibliographique
Résumé
The intelligent mobile phone has become the most widely used communications device globally and the access device of choice in the developing world. In countries like India it is often the only available device for accessing the Internet and its large variety of associated services. This panel will focus on the impact of the widespread penetration and use of intelligent mobile devices in major developing countries, specifically China, India, Mexico and Brazil. China and India are the largest and second largest mobile markets in the world, with some 1.3 billion and 1.2 billion mobile subscriptions respectively, while Mexico and Brazil are two important mobile markets in Latin America. These developing countries have leapfrogged directly into ubiquitous mobile communications networks, although an urban-rural gap remains in the deployment and use of broadband mobile communications networks. The panel will discuss issues such as: • What role does mobile broadband play in different national broadband strategies? In particular, how can it be used to narrow the urban-rural infrastructure gap by providing ubiquitous “last mile” access? • In addition to efficiently allocating and managing the use of the spectrum, what other roles can governments play in enabling the continued growth of mobile communications services? • What strategies have these developing countries adopted in facilitating the national deployment of broadband mobile communications infrastructure or wholesale networks? Do Public Private Partnerships have a role to play in such deployments? How can demand side strategies be used to complement supply side initiatives? • What strategies have developing countries adopted towards mobile standard-setting and device manufacturing? • What role can mobile broadband play in the delivery and use of a wide variety of digital information and transactional services, including electronic payments? How could mobile broadband services compensate for deficiencies in the physical infrastructure for banking services, rural healthcare and public information? • Can governments facilitate the transition towards a Digital Economy by becoming Model Users of online information and transactional services, particularly services which affect small businesses, consumers and citizens? The authors, whose expertise covers various countries and regions, will discuss and compare strategies being used in developing countries like China, India, Mexico and Brazil. We wish to find out what has worked, what did not, the problems encountered and whether there are lessons to be learned that are of general applicability, as well as for particular countries. We wish to explore the possibilities and limitations of learning from other nations’ experiences, identifying common policy challenges and medium-term research requirements of interest to the TPRC community. Panel Moderator: Dr. Prabir Neogi, Visiting Fellow, Carleton University, Ottawa, Canada Panelists and suggested areas of coverage: Prof. Erik Bohlin, Professor of Technology Management and Economics, Chalmers University of Technology, Sweden [E.U. developments and comparisons]; Prof. Rekha Jain, Professor, Indian Institute of Management, Ahmadabad (IIMA) and Executive Chair of the IIMA-IDEA Telecom Centre of Excellence (IITCOE), India [India]; Prof. Krishna Jayakar, Co-Director, Institute for Information Policy and Co-Editor, Journal of Information Policy, Donald P. Bellisario College of Communications, Penn State University, US [China]; Prof. Judith Mariscal Aviles, Professor, Centro de Investigacion y Docencia Economica (CIDE), Director of the Telecommunications Research Program Telecom-CIDE, and member of the Steering Committee of DIRSI, Mexico[Mexico & Brazil]; Prof. Roxana Barrantes Caceres, PUCA, Peru [Latin America].
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 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».