Bibliographic record
Abstract
With a global push towards achieving the Paris Agreement as an imperative, there is a growing focus on how this will be achieved, and the role of organisations in supporting the transition to a net-zero economy and broader sustainability action. Investment decision-making is increasingly being informed by sustainability information, with stakeholders demanding greater transparency and comparability of disclosures, especially climate-related disclosures from organisations typically categorised as heavy emitters. This has ramifications for the oil and gas industry. Today, the sustainability reporting landscape is complicated, with a proliferation of sustainability disclosure frameworks developed by various standard setters. In an effort to standardise disclosures, the International Financial Reporting Standards (IFRS) Foundation launched the International Sustainability Standards Board (ISSB) in November 2021 to develop baseline global sustainability reporting standards including for climate disclosure (based on Taskforce on Climate-Related Financial Disclosures). We have also seen proposals from The US Securities Exchange Commission on climate-related disclosures, which have sparked extensive public comment. Locally, the Government has committed to introducing standardised internationally aligned reporting requirements, including climate-related disclosures, which will be mandatory for certain entities. Regardless of exactly where each jurisdiction lands, it is clear that the sustainability reporting requirements will be significant, leading to increased costs for reporters, resulting in a business imperative to leverage value creation from this compliance transformation. Indeed, organisations putting sustainability at the heart of their business strategy will be the game changer in the new sustainable and equitable energy transition. And just as there will be leaders, there will also be laggards who risk value erosion.
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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.010 | 0.060 |
| Scholarly communication | 0.048 | 0.062 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".