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Record W1604456389 · doi:10.1057/9781137301925_9

Financing Businesses in Africa: The Role of Microfinance

2013· book-chapter· en· W1604456389 on OpenAlex
Shilpa Aggarwal, Leora Klapper, Dorothe Singer

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

VenuePalgrave Macmillan UK eBooks · 2013
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCollateralMicrofinancePovertyJoint and several liabilityCapital (architecture)EconomicsArgument (complex analysis)Magic bulletBusinessDevelopment economicsMarket economyFinancial systemLiabilityFinanceEconomic growth

Abstract

fetched live from OpenAlex

The law of diminishing marginal productivity dictates that scarce resources earn a high return. Why then, does capital not flow to the poor, its most productive users? This has been attributed in part to the failure of credit markets. The argument goes that the poor have so little to offer by way of collateral, and borrow such small amounts, that it is too risky and expensive to lend to them. The ramification is that they get caught in a credit-based poverty trap, wherein they are unable to undertake profitable investments due to credit constraints and hence, remain poor. The great promise of microcredit — making joint-liability loans to small groups of poor people possessing no collateral, enabling them to make productive investments — was to be the magic bullet against poverty. Yet, a mere five years after the Nobel Peace Prize was awarded to Muhammad Yunus and the Grameen Bank, claims about microcredit’s transformative power are being debated.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.190
Teacher spread0.168 · 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