Environmental Corporate Social Responsibility Reporting in the Oil Sands: New Directions
Bibliographic record
Abstract
The environmental performance of oil sands development in Canada is under intense public scrutiny. The prevailing narrative positions industry development with an essential contribution to Canada's economy and energy security against potential environmental damage and negative impacts on communities. This research paper studies whether the range of current corporate social responsibility (CSR) indicators used by oil sands companies effectively addresses the main environmental externalities associated with oil sands development. This research finds that, while improvements have been made, there are still significant gaps in reporting methods. This paper then explores ways to both improve these CSR reports and suggests drivers (policy or other) that will help incentivize a more consistent release of information on environmental externalities by corporations. The suggestions for improvement are placed in the context of communicating more effectively with a broad range of multiple stakeholders. Finally, this paper concludes by discussing how improved environmental CSR reporting in the oil sands will not only help enhance the industry's "social license to operate," but will also contribute to our understanding and advancement of the political and socio-economic setting in which oil sands development occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".