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Record W2893971662 · doi:10.1111/basr.12150

A Corporate Social Responsibility Analysis of Payday Lending

2018· article· en· W2893971662 on OpenAlexaff
Mark S. Schwartz, Chris Robinson

Bibliographic record

VenueBusiness and Society Review · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
Fundersnot available
KeywordsLoanSocial responsibilityGovernment (linguistics)BusinessCorporate social responsibilityPosition (finance)ExploitPerspective (graphical)Product (mathematics)Face (sociological concept)AccountingFinancePublic relationsSociology

Abstract

fetched live from OpenAlex

Abstract In this article, we use a corporate social responsibility (CSR) framework to analyze the payday loan industry by critically examining its practices from an economic, legal, and ethical perspective. Payday loans are essentially a very high cost, unsecured, short‐term personal loan. Given the inherent nature of the product being offered, the industry appears on the face of it to be in a position to potentially exploit vulnerable consumers in pursuit of profits. With this concern in mind, our analysis investigates the following three issues: Can the payday loan industry currently be considered to be acting in a socially responsible manner? If the industry cannot be considered to be socially responsible, should it be further regulated? If the industry should be further regulated, how should it be regulated? To address these issues, we first provide a brief historical overview of payday loans. Second, we describe the important characteristics of payday loans and how the industry operates. Third, we draw on various sources of evidence to demonstrate that the payday loan industry, while fulfilling its basic economic obligations, falls outside of both the legal and ethical domains of the Three Domain Model of CSR. Based on our analysis, our conclusion is that the payday loan industry requires additional government legal regulation, particularly with respect to allowable fees, and we conclude that there is a strong ethical case for banning payday loans altogether.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.830

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.006
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.067
GPT teacher head0.304
Teacher spread0.238 · 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.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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