A Corporate Social Responsibility Analysis of Payday Lending
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".