MétaCan
Menu
Back to cohort
Record W4410732839 · doi:10.1016/j.bir.2025.05.013

Network readiness, financial inclusion, and sustainable development goals: Insights from a clustering approach

2025· article· en· W4410732839 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

VenueBorsa Istanbul Review · 2025
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersFP7 Coordination of Research ActivitiesHorizon Therapeutics
KeywordsInclusion (mineral)Financial inclusionCluster analysisSustainable developmentBusinessFinancePsychologyComputer scienceFinancial servicesPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This research examines the role of the financial sector in advancing the Sustainable Development Goals (SDGs) by promoting access to financial services and leveraging Information and Communication Technologies (ICTs). Utilizing the k-means++ algorithm, we clustered 41 European countries based on the values of the Network Readiness Index (NRI) pillars—serving as a measure of ICT—and by the achieved values for SDG indicator 8.10, which reflects access to financial services. The results confirmed that cluster differences based on NRI components are significant, particularly with respect to: ownership of accounts with banks, other financial institutions, and mobile-money-service providers; sociodemographic characteristics of financial service users; and contributions to 13 out of the 17 SDGs. Notably, in terms of the impact of financial service access on SDG performance, significant differences between clusters were found in eight out of the 17 SDGs.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.853
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.004
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 head0.009
GPT teacher head0.218
Teacher spread0.209 · 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