Mobile Communications Policies and National Broadband Strategies in Developed and Developing Countries: Lessons, Policy Issues and Research Challenges
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. The International Telecommunications Union’s report “The World in 2015: ICT Facts and Figures” estimates that there were some 7.1 billion mobile service subscriptions by the end of 2015, corresponding to a global population of some 7.4 billion. Mobile cellular penetration rates stand at 97% globally, 121% in developed and 92% in developing countries. Mobile broadband is the most dynamic market segment. There are now three times as many mobile broadband subscriptions as fixed ones.Spectrum is the lifeblood of mobile communications services. As high-speed mobile Internet access becomes more readily available and affordable, mobile devices are being used widely for a variety of bandwidth-hungry applications. Therefore the demand for additional spectrum bandwidth is likely to increase rapidly and outstrip the supply for the next few years. Issues related to spectrum allocation and management have become an important component of any national wireless broadband strategy.Mobile Internet access and use is becoming the new norm. Policy makers in developing countries, in addition to dealing with the conventional issues related to facilitating the growth of mobile communications (e.g. spectrum availability and re-allocation, infrastructure sharing and interconnection, service pricing and availability), will need to formulate policies, and regulations where required, to address the new challenges related to the large scale migration of mobile users from 2G to 3G/4G broadband communications networks. Many governments have instituted a range of supply side policies to accelerate broadband deployment, increase availability and reduce costs. However, the most effective design of complementary demand side policies remains uncertain. This international panel will focus on the impact of the widespread penetration and use of intelligent mobile devices, in both developing and developed countries. The panel will discuss issues such as:• What role does mobile broadband play in different national broadband strategies, and how is it integrated with the wireline component?• Other than efficiently allocating and managing the use of the spectrum, what other roles can governments and regulators play in enabling the continued growth of mobile telecommunications services? • Last but not least, what conceptual frameworks do researchers and policy-makers use when shaping communications policy? What is the role of evidence in shaping current approaches? The Panelists, whose expertise covers various countries and regions, will discuss and compare strategies being used in developed countries like the US, Australia and the EU, and developing countries like Mexico, Brazil and 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 particular countries. We wish to explore the possibilities and limitations of learning from other nations’ and regions’ experiences, identifying common policy challenges and medium term research requirements of interest to the TPRC community.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".