Rating and Evaluating the Combined Financial and Environmental Performance of Companies in the Metals and Mining Sector
Bibliographic record
Abstract
The basic intent in financial performance evaluation is to appraise current business operations internally and to benchmark them against similar business operations externally in order to identify best-in-class practices. In this paper, a new positive environmental/financial screening approach that can simultaneously include a wide combination of regulatory, technological, operational and event dimensions is created for the analyzing, rating, ranking, benchmarking, and selecting of companies from an industry sector. A method is provided that advances the use of Data Envelopment Analysis (DEA) for rating and ranking diverse groups of companies using a combination of both financial and environmental performance measures. This novel approach proceeds by first stratifying the sector into comparably efficient groups of companies through the construction of a series of efficient DEA frontiers, and then by ranking each company within these groups relative to DEA-based contextual attractiveness measures calculated from the different partitions. The method is illustrated through an application to a group of companies from the Metals and Mining Industry sector.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".