Bibliographic record
Abstract
Sustainability reporting for non-financial disclosure is not new, but recently environmental, social, governance (ESG) publications have become critical to attracting investors. In particular, industry-specific disclosures on topics such as greenhouse gas (GHG) emissions have become more prevalent as environment, climate change, and social consciousness are highlighted in mainstream media. In addition, investors are calling for standardization of ESG disclosure by the Canadian energy industry. In this report, I consider the progress made in ESG framework standardization and the type and accuracy of GHG disclosures by conducting a content analysis of 26 Canadian energy company ESG reports. I find that the industry has shown an increase in its due diligence in many aspects of ESG reporting, including the disclosure of Scope 1 and 2 emissions footprints and intensities. Improvements in reporting are needed for metrics such as Scope 3 emissions, GHG quantification methodologies and data verification, and emission reduction strategies.
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 | 0.025 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.003 |
| 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".