Organizations' Sustainability Reports: A Critical Analysis and Framework for (Better) Best Practices
Bibliographic record
Abstract
This study examines the sustainability reporting practices of a diverse sample of Canadian organizations that are named as leaders in corporate sustainability. We conducted an in-depth content analysis to identify key themes, such as the strategic alignment of environmental sustainability reporting with the associated impacts, emphasis on community-giving, and offering employee-centered social practices. Moreover, we expanded upon these findings by critically analyzing the sustainability reporting practices of these organizations to develop tangible recommendations for corporate practitioners, third-party rating organizations, and academic researchers. These recommendations are synthesized into a practical framework that draws upon examples from our dataset to provide targeted principles for effective corporate sustainability reporting, which we hope will highlight the need for—and steps to achieve—more effective corporate sustainability practice, reporting, assessment, and research. Ultimately, our findings suggest that companies, and third-party rating organizations alike, can do more toward reaching objectives concerning public accountability, and demonstrating a tangible commitment to sustainability.
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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 | 0.349 | 0.352 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.049 | 0.023 |
| Science and technology studies | 0.023 | 0.064 |
| Scholarly communication | 0.049 | 0.041 |
| Open science | 0.010 | 0.018 |
| Research integrity | 0.006 | 0.013 |
| 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".