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Record W2003708469 · doi:10.4018/jgim.2014010103

ICT Helping to Scale up Microfinance

2014· article· en· W2003708469 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

VenueJournal of Global Information Management · 2014
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsMicrofinanceGeneral partnershipBusinessBusiness modelArgument (complex analysis)Financial servicesService (business)Developing countryScale (ratio)FinanceMarketingEconomic growthEconomics

Abstract

fetched live from OpenAlex

Finding ways to downscale microfinance is one of the current challenges facing commercial banks, especially in developing countries. As banks have a poor knowledge of microfinance, operating in this market will require capacity-building, innovative business models and new technological architectures. This paper discusses how one particular architecture – the Brazilian model of correspondent banking (CB) – is helping banks cope with these challenges. Since the model was created, in 2000, it has allowed banks to downscale financial services outside their traditional branches and establish successful partnerships with local microfinance institutions (MFIs). The authors focus on one particular case involving a partnership between an accredited MFI (Banco Palmas) and two major banks (Banco do Brasil e Caixa Econômica Federal), to make the argument that the Brazilian CB model represents an innovation at the “meso level”, defined by Helms (2006) as the infrastructure comprising a network of service providers necessary to the operation of MFIs.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.226
Teacher spread0.221 · 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