Assessing Adaptations to Global Transformational Events in Canadian Corporate Social Responsibility Practices
Bibliographic record
Abstract
This dissertation investigates the following question: do global transformational events result in transient or transformational changes in Corporate Social Responsibility (CSR) practices? The novel concept of global transformational events is defined as pivotal incidents—both endogenous and exogenous—with profound global repercussions, creating catalysts that inherently drive shifts in corporate operations and global market dynamics. Adapting the PICOT framework from clinical health research, this dissertation assesses the impact of global transformational events on CSR. PICOT stands for Population, Intervention, Comparison, Outcome, and Time, and it provides a structured format for formulating research questions in evidence-based practice. This approach helps to compare changes in corporations' CSR initiatives before and after global transformational events. The data used within this work is gleaned from a diverse range of sources including interviews with industry representatives, annual reports, and public records. The dissertation spans eight chapters. Chapter 1 introduces the research theme, while Chapter 2 reviews the theoretical foundation of CSR decision-making in both stable and volatile operating environments. The heart of the dissertation, Chapters 3 through 6, is rooted in empirical case studies. Chapters 3 and 4 assess the impact of the COVID-19 pandemic on CSR initiatives within Canada, with a cross-sector overview in the former and a specific focus on the automotive manufacturing sector in the latter chapter. Chapter 5 evaluates the influence of the Paris Agreement on decarbonization commitments in Canada's automotive manufacturing sector. Chapter 6 examines the role of the United Nations Sustainable Development Goals in guiding community investment decisions by leading Canadian private sector companies. The emerging domain of sustainability management and its potential to augment CSR practices is the focus of Chapter 7. Chapter 8 then synthesizes the findings, highlighting contributions to knowledge, theory, and practice, as well as outlining future research directions. In sum, this dissertation examines the degree to which CSR initiatives of large firms operating in Canada are influenced by global transformational events, while underscoring prevailing corporate tendencies to gravitate towards a "business as usual" mindset. This inclination persists even when external operating circumstances have undergone dramatic shifts, suggesting a resistance to adapt to new paradigms. This pattern underscores a gap between the potential for—and the realization of—sustained CSR changes in response to global transformational events, encouraging further scrutiny of corporate behaviour to ensure meaningful alignment of corporate operations with environmental and societal wellbeing.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".