From Responsibility to Requirement: COVID, Cars, and the Future of Corporate Social Responsibility in Canada
Bibliographic record
Abstract
The COVID-19 pandemic has caused significant impacts to the automotive manufacturing industry. Despite substantial financial uncertainty, disruptions to supply chains, and shutdowns of manufacturing operations, automotive firms supported crisis response efforts throughout the course of the pandemic. Drawing on interviews with all the consumer automotive manufacturing companies in Canada (Ford, General Motors, Honda, Stellantis, and Toyota) as well as the two largest global automotive parts suppliers operating in Canada (Linamar and Magna), we investigated whether voluntary corporate responses to COVID-19 will shape long-term corporate social responsibility programs or simply constitute one-off crisis management actions. Ultimately, we argue that while Canada’s pandemic response efforts have benefitted from the voluntary involvement of automotive manufacturing companies, the limited coordination between stakeholders underscores the need for greater public sector oversight of the relationship between society and the private sector. To ensure preparedness for meeting new challenges, such as climate change, we call for the era of voluntary corporate social responsibility programs to yield to a period of corporate social requirements.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".