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Record W3028733537 · doi:10.1073/pnas.1909326117

Leveraging mobile phones to attain sustainable development

2020· article· en· W3028733537 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMobile phoneDisadvantagedEmpowermentGeospatial analysisBusinessReproductive healthPovertyInternet privacyPopulationEconomic growthComputer scienceEnvironmental healthGeographyTelecommunicationsEconomicsMedicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.281
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it