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Record W3207059856 · doi:10.1108/srj-02-2021-0061

Micro-loans and financial performance: a case of a Chinese commercial bank

2021· article· en· W3207059856 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

VenueSocial Responsibility Journal · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of New BrunswickCape Breton University
Fundersnot available
KeywordsProfitability indexOriginalityLoanBusinessCorporate social responsibilityCommercial bankAccountingValue (mathematics)ChinaReturn on assetsFinancial systemFinanceQualitative research

Abstract

fetched live from OpenAlex

Purpose This study aims to investigate the relationship between a commercial bank’s micro-loaning activity and overall performance over a 10-year period. Design/methodology/approach Quarterly data was obtained from the Wind Database, China Minsheng Banks’s official annual reports and annual corporate social responsibility reports from 2009 to 2019, to test the linear relationship between micro-loan activities and the overall financial performance of the bank. Findings The results of this study empirically demonstrate that there is a positive relationship between increases in micro-loaning activity and the overall performance of the bank. Some key recommendations for the sector are shared in the conclusion of this paper. Originality/value In the financial sector, some corporate social responsibility activities focus on the issuance of micro-loans. It is unclear, however, if this has also served as a means to increase profitability and overall performance for such institutions.

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)0.004
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.022
GPT teacher head0.284
Teacher spread0.263 · 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