<scp>The Arctic Environmental Responsibility Index</scp>: A method to rank heterogenous extractive industry companies for governance purposes
Bibliographic record
Abstract
Abstract The Arctic Environmental Responsibility Index (AERI) covers 120 oil, gas, and mining companies involved in resource extraction north of the Arctic Circle in Alaska, Canada, Greenland, Finland, Norway, Russia, and Sweden. It is based on an international expert perception survey among 173 members of the International Panel on Arctic Environmental Responsibility (IPAER), whose input is processed using segmented string relative ranking (SSRR) methodology. Equinor, Total, Aker BP, ConocoPhillips, and BP are seen as the most environmentally responsible companies, whereas Dalmorneftegeophysica, Zarubejneft, ERIELL, First Ore‐Mining Company, and Stroygaz Consulting are seen as the least environmentally responsible. Companies operating in Alaska have the highest average rank, whereas those operating in Russia have the lowest average rank. Larger companies tend to rank higher than smaller companies, state‐controlled companies rank higher than privately controlled companies, and oil and gas companies higher than mining companies. The creation of AERI demonstrates that SSRR is a low‐cost way to overcome the challenge of indexing environmental performance and contributing to environmental governance across disparate industrial sectors and states with divergent environmental standards and legal and political systems.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".