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Record W3210283822

Mobile Communications, the Internet and the Digital Economy: Comparisons and Lessons from Four Major Developing Countries - China, India, Mexico and Brazil

2018· article· en· W3210283822 on OpenAlexaffabout
Prabir K. Neogi

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsCarleton University
Fundersnot available
KeywordsDeveloping countryTelecommunicationsMobile broadbandInternet accessBusinessMobile phoneMobile telephonyDigital divideThe InternetChinaLast mile (transportation)Mobile technologyMobile paymentMobile computingEconomic growthEngineeringGeographyMobile radioComputer scienceWirelessEconomicsWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

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].

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.009
GPT teacher head0.252
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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