Intelligent Mobile Devices and Their Impact: Perspectives, Lessons, Issues and Challenges
Notice bibliographique
Résumé
Panel Moderator: Dr. Prabir Neogi, Visiting Fellow, Carleton University Proposed Panellists: Prof. Alison Gillwald, Director, Research ICT Africa! & Associate Director, The EDGE Institute, Johannesburg, South Africa Prof. Rekha Jain, Professor, Indian Institute of Management, Ahmedabad (IIMA) and Executive Chair of the IIMA-IDEA Telecom Centre of Excellence (IITCOE) Prof. Judith Mariscal, Professor, Centro de Investigacion y Docencia Economica (CIDE), Director of the Telecommunications Research Program Telecom-CIDE, and member of the Steering Committee of DIRSI Prof. Catherine Middleton, Professor, Ted Rogers School of Information Technology Management, Ryerson University, Toronto, Canada Dr. Jean-Paul Simon, Senior scientist and Consultant to the Information Society Unit, Directorate-General JRC, IPTS, European Commission and member EuroCPR Board Use of the increasingly intelligent mobile phone has exploded in recent years. It has become the most widely used communications device in the world, and the access device of choice in the developing world. The ITU estimates that there were some 6 billion mobile service subscriptions by the end of 2011, some 86% of the global population. ITU estimates indicate that mobile broadband services grew by some 40% worldwide in 2011 and that there are now twice as many mobile broadband subscriptions as fixed ones. The Boston Consulting Group forecasts that by 2016, mobile devices such as smartphones and tablets could account for four out of five broadband connections. Smartphones such as the Apple iPhone and its many competitors are already in widespread use in many countries, tablet computers are becoming increasingly popular and laptops now compete with desktop PCs in functionality. As high-speed mobile Internet access becomes more readily available and affordable, the smartphone and other handheld devices are widely being used for business applications as well as for personal and social purposes. This means that the demand for additional spectrum bandwidth, which is the lifeblood of mobile communications services, is likely to outstrip the supply for the next few years. Governments have a key role in efficiently allocating and managing the use of the spectrum (e.g. through well designed auctions, re-farming valuable spectrum released by the conversion from analogue to digital TV broadcasting, shared and license-exempt spectrum use regimes) and meeting the demand for additional spectrum bandwidth. Issues and challenges related to the efficient allocation and management of the spectrum will become an important component of any national broadband strategy. This panel will focus on the socio-economic impact of the cell phone and other more intelligent mobile devices in both developing and developed countries, the role that wireless access and mobile broadband play in various national and regional broadband strategies, and how wireless communications is integrated with the wireline component of such strategies. The proposed panelists will discuss strategies being used in Australia, the EU, the US, South Africa, Latin American countries like Brazil and Mexico, South Asian countries like India, among others. 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 the US and Canada. At the same time, we would like to explore the possibilities and limitations of learning from other nations’ and regions’ experiences. A dialogue between the policymakers and researchers could help to identify current and future policy issues which will require further research work.
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 | 0,009 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,008 |
| Communication savante | 0,018 | 0,024 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,011 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,004 |
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 ».