Explicit and Implicit CSR: An Exploration of the Canadian Energy Sector
Bibliographic record
Abstract
Corporate Social Responsibility (CSR) has evolved into a fundamental component of corporate identity and stakeholder engagement since the 1950s. Research has focused on cross-national and industry comparisons without much attention to potential subnational and intra-industry differences. This thesis examines how CSR evolves over time through the interaction of regional institutional contexts and global societal expectations. The longitudinal comparative case study design explores CSR narratives of two Canadian energy firms, Hydro-Québec in Québec and Suncor in Alberta between 2010 and 2024. Annual reports and press articles are used for qualitative content analysis to assess firm self-presentation and media framing. This thesis builds on Matten and Moon’s (2008, 2020) frameworks on explicit and implicit CSR including institutional theory and National Business Systems (NBS). Findings show that firms in the same industry and country, regardless of the nature of CSR will exhibit explicitization over time. However, this process operates through divergence and convergence. Across dispersed territories, institutional proximity outweighs industry affiliation. Institutional proximity will create different CSR foci intra-industry because of ownership and regional influences. However, CSR discourse will show more convergence on global themes like clean energy, emissions reduction, and Indigenous reconciliation. News outlets often reflect these narratives, while exposing differences between self-presentation and perception. Through the longitudinal, subnational, and intra-industry lens, this thesis presents the ever-changing nature of CSR communication affected by institutional logics, ownership, and regional context. Contributions to the field include emphasis on subnational differences, multi-source analysis, changes in CSR, and the existence of intra-industry variation.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".