COVID‐19 and credit unions: CSR approaches to navigating the pandemic
Bibliographic record
Abstract
Abstract The financial sector plays a fundamental role in Canadian society; credit unions, in particular, cater to a specific group of stakeholders not commonly served by traditional financial institutions. This research investigates the social responsiveness (CSR 2 ) approaches implemented by credit unions during the pandemic, the type of actions implemented, the stakeholders assisted, and whether the size of credit unions may affect their responses. Data were collected from the 100 largest credit unions from nine Canadian provinces and assessed through qualitative content analysis. Results show that Canadian credit unions have implemented accommodative and proactive approaches when addressing COVID‐19, through more operational than financial actions directed to their clients and employees, and that those with larger assets implement a greater number of actions compared to credit unions with smaller assets. More importantly, results show that traditional CSR 2 approaches (e.g., RDAP) do not fit unexpected crises, so novel approaches are required to face future crises and remain resilient. While we aim to contribute to the body of literature by examining how credit unions have assisted their stakeholders during the pandemic, we also, and most importantly, seek to provide material for discussing and reflecting on how organizations are prepared to face crises that will likely arise in the future.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".