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Record W2591717757 · doi:10.1080/00346764.2017.1300316

Trust, cultural norms and financial institutions in rural communities: the case of Cameroon

2017· article· en· W2591717757 on OpenAlexaff
Nathanael Ojöng

Bibliographic record

VenueReview of Social Economy · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsTyndale University
Fundersnot available
KeywordsBusinessFinancial instrumentCultural valuesFinanceSociologyEconomicsEconomic systemSocial science

Abstract

fetched live from OpenAlex

The success of the operations of formal and informal financial institutions (IFIs) hinges on a high degree of trust. The pivotal role of trust warrants careful analysis regarding its formation in these financial institutions. Using the case of Cameroon, the paper interrogates trust development between formal financial institutions and their clients, and between IFIs and their members. Trust formation occurs via certain cognitive trust-building processes: calculative, prediction, intentionality, capability, and transference processes. The paper argues that trust formation through these processes is predicated upon cultural values and beliefs. It is precisely because of cultural norms that traditional leaders play a role in ensuring that loans granted by formal financial institutions are repaid, thereby serving as principal actors in the functioning of financial capitalism in rural areas. The interplay between culture and financial institutions reconfigures the financial architecture in rural zones. Culture creates a social relational anthropology that is significant for how financial institutions operate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.006
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.301
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations14
Published2017
Admission routes1
Has abstractyes

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