Making money move: an analysis of corporate social responsibility activities in money transfer firms
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.
Bibliographic record
Abstract
Abstract Although research has examined the financial sector’s response to COVID-19, the role of the cross-border money transfer industry remains unclear. This study investigates the corporate social responsibility (CSR) actions of 22 cross-border money transfer firms headquartered in Canada, the US, and the UK to determine how they supported stakeholders during the pandemic. Using qualitative data analysis software, we analyze textual data from company websites, press releases, and blogs to assess CSR activities. Our findings show that nine of the 22 cross-border money transfer firms engaged in COVID-related CSR efforts and communicated these actions through their controlled channels. Two out of every three firms that publicized their CSR initiatives during the pandemic were not traded in any stock exchange market. This research has two key implications. First, disclosing CSR initiatives through controlled or uncontrolled channels increases the likelihood of attracting socially conscious customers and investors, which could ultimately lead to higher economic profits. Second, economic profit can create a bandwagon effect, encouraging other money transfer firms to integrate CSR activities into their business models, which may enhance the well-being of the communities they serve and rely upon.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it