Sustainability reporting and strategic legitimacy: The influence of operating in emerging economies on the level of GRI reporting in Canada’s largest companies
Bibliographic record
Abstract
Corporate sustainability reporting is a contributor to strategic legitimacy (Chelli, Durocher, & Fortin, 2018) and certain traditional corporate characteristics (size, industry vulnerability) can influence the level of sustainability reporting (Drempetic, Klein, & Zwergel, 2020). However, limited literature exists in regards to sustainability reporting by Canadian companies operating in emerging countries. Content analysis of sustainability reports examined the current use of the Global Reporting Initiative (GRI) framework. Principal component analysis (PCA) provided a sustainability reporting index (SRI) measure for each firm using factor scores. Correlations and independent-samples t-testing tested the association of the level of reporting to a firm’s size, industry, level of internationalization, and level of activity in emerging economies. A review of 234 large Canadian-based, publicly-traded companies found a total of 86 companies employed the GRI framework, and data from these companies was used in this study. Asset size and vulnerable industries had no significant association with the level of sustainability reporting contrary to prior studies. Operating in emerging economies resulted in greater levels of sustainability reporting when compared to firms that do not. This finding is consistent with the external legitimacy strategy and contributes to the limited literature in this area
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".