Intelligent Mobile Devices and Their Impact: Perspectives, Lessons, Issues and Challenges
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".