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Record W3154361730 · doi:10.24908/iqurcp.9846

A Different Kind of Subprime: Risk Management in Microcredit Lenders and their Implications on Debtors

2018· article· en· W3154361730 on OpenAlexvenueno aff
Graeme Mckinnon-Nestman

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsMicrofinanceMainstreamFinancial inclusionBusinessEntrepreneurshipFinancial servicesStatus quoScope (computer science)Small businessRisk managementFinancial innovationFinanceEconomicsEconomic growthMarket economyPolitical science

Abstract

fetched live from OpenAlex

Microfinance is a movement which aims to promote financial inclusion and empower individuals through small loans (as well as other services) to finance business ventures in the developing world and beyond. Microfinance aims to meet the financial needs of individuals who are left out of the scope of more mainstream financial services, while avoiding the perceived shortfalls of traditional aid such as dependence. Along with the expansion of microfinancial institutions (MFIs) since the turn of the century and the proliferation of the internet, diligent and prudent management of these institutions has never been of greater importance. Though there is a lot of research on entrepreneurship, business, finance, andmanagement concerning more mainstream practices, it is clear that microfinance is at a frontier of modern commerce. Risk is basic to all business (as well as life in general) and in order for the microfinance movement to maintain its growth, it must be self-sustaining while maintaining its ability to assist meaningful development. This presentation will compare the risk management practices standard to MFIs now, as well as look at how risk is fundamentally different to small entrepreneurship in developing regions in comparison to developed economies. In doing so, it should shed light on the financing needs and realities of target individuals and see how MFIs in the status quo are able to meet them. From this we should seewhere the shortfalls currently lie and where things may be improved.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.103
GPT teacher head0.327
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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