Mobile at the Bottom of the Pyramid: Informing Policy from the Demand Side
Bibliographic record
Abstract
There has been a massive increase in the global use of mobile phones, especially in the developing world. It has been said that the diffusion of mobile telephony has been the fastest for any information and communication technology in human history (Kalba, 2008). It has drawn some scholarly attention (Donner, 2008), but perhaps not commensurate with the scale of the phenomenon and the way in which it involved the poor in the developing world on a scale not seen before. The one attempt at a magisterial review (Castells et al., 2007) fell short because it reported data only up until 2004, before the mobile boom accelerated in the developing world, as demonstrated by Figure 1, which shows the mobile SIMs per 1001 for three South Asian countries that are featured in almost all the articles in this issue and account for almost a quarter (1.5 billion people) of the world’s population, as well as most of its poor. The articles in this issue will contribute to alling that lacuna.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.017 | 0.037 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.026 | 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".