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CSR Programs of Financial Institutions

2022· book-chapter· en· W4285738756 on OpenAlexaff
Mirela Panait, Lukman Raimi, Eglantina Hysa, Abiodun S. Isiaka

Bibliographic record

VenueAdvances in logistics, operations, and management science book series · 2022
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsOrder (exchange)BusinessInclusion (mineral)Set (abstract data type)PopulationFinancial inclusionCorporate social responsibilityAccountingPublic relationsFinancePolitical scienceFinancial servicesSociologySocial science

Abstract

fetched live from OpenAlex

This chapter explores the contrary views on CSR activities of financial institutions by drawing attention to the purported chameleon behavior of banks in promoting various CSR programs, adopting equator principles in lending activity, conducting financial education campaigns to increase the degree of financial inclusion of the population versus the claim about deceptive promotional techniques, practicing abusive contractual clauses in order to maximize profits at the expense of consumers. The chapter is distinguished by the critical attitude towards the behavior of FTNCs which knows significant differences depending on the area of manifestation – in the country of origin or in the host countries, developing countries. In addition, these entities take advantage of international instruments set up such as the equator principles or non-financial reporting standards to create a positive image among stakeholders, although their behavior is not socially responsible.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.038
GPT teacher head0.285
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 designNot applicable
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

Citations3
Published2022
Admission routes1
Has abstractyes

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