Leveraging mobile phones to attain sustainable development
Bibliographic record
Abstract
For billions of people across the globe, mobile phones enable relatively cheap and effective communication, as well as access to information and vital services on health, education, society, and the economy. Drawing on context-specific evidence on the effects of the digital revolution, this study provides empirical support for the idea that mobile phones are a vehicle for sustainable development at the global scale. It does so by assembling a wealth of publicly available macro- and individual-level data, exploring a wide range of demographic and social development outcomes, and leveraging a combination of methodological approaches. Macro-level analyses covering 200+ countries reveal that mobile-phone access is associated with lower gender inequality, higher contraceptive uptake, and lower maternal and child mortality. Individual-level analyses of survey data from sub-Saharan Africa, linked with detailed geospatial information, further show that women who own a mobile phone are better informed about sexual and reproductive health services and empowered to make independent decisions. Payoffs are larger among the least-developed countries and among the most disadvantaged micro-level clusters. Overall, our findings suggest that boosting mobile-phone access and coverage and closing digital divides, particularly among women, can be powerful tools to attain empowerment-related sustainable development goals, in an ultimate effort to enhance population health and well-being and reduce poverty.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".