Mobile Communications, the Internet and Digital India: A Developing Country Approach
Bibliographic record
Abstract
The intelligent mobile phone has become the most widely used communications device in the world and the access device of choice in the developing world. In countries like India, which has become the second largest mobile phone market in the world, it is often the only available device for accessing the Internet and its large variety of associated services. This paper will focus on the impacts of the widespread penetration and use of intelligent mobile access devices in India, combined with the deployment of mobile broadband networks. Issues discussed include: • In a developing country like India, what role does mobile broadband play in its national broadband strategy? • How can mobile broadband be used to narrow the stark 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 and regulators play in enabling the continued growth of mobile communications services? • What strategies have developing countries like India adopted in facilitating the national deployment of broadband mobile communications infrastructure or wholesale networks? Do Public Private Partnerships have a role to play? • 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 can mobile broadband services compensate for deficiencies in the physical infrastructure for banking services, rural healthcare and public information? • How can the Central and State governments facilitate the demand side of the transformative Digital India initiative by becoming Model Users of online information and transactional services, particularly services which affect small businesses, consumers and citizens? In India, mobile subscribers constitute some 98% of the 1.2 billion total telephone subscribers. Urban Tele-density is 3 times greater than Rural Tele-density, and Urban Internet subscribers per 100 populationare some 5 times greater than Rural. The deployment of mobile broadband networks is limited, especially outside urban areas. Some 60% of mobile users only have a feature phone, such numbers being larger in rural areas. The gaps in Tele-density, broadband mobile network deployments and smart phone adoption indicate the magnitude of the stark and growing urban-rural digital divide in India. As mobile Internet use combined with electronic transactions and payments becomes the new norm, policy makers will need to formulate new policies, to address challenges related to the large-scale migration of mobile users to broadband networks and the use of Internet-based transactional services. How can “Demand Pull” policies complement “Supply Push” initiatives? Many governments have instituted a range of supply side policies to accelerate broadband deployment, increase availability and reduce costs. However, the effective design of complementary demand side policies remains uncertain, particularly security policies for maintaining the integrity of transactional services against cyber-attacks and cyber-fraud. The TPRC community wishes to find out what has worked, what has not and whether there are lessons to be learned that are of general applicability, as well as for a particular developing country like India.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".