Measuring sustainability performance with SWARA-MEREC-COBRA multi-criteria model: A case study of Anadolu insurance company
Bibliographic record
Abstract
The aim of this study is to assess and rank Anadolu Sigorta Company's sustainability performance between 2018 and 2022 using the hybrid SWARA-MEREC-COBRA model. The sustainability performance evaluation criteria's importance weights were determined using both subjective and objective methods. The SWARA algorithm was used to determine the weights subjectively based on expert opinion, while the MEREC algorithm was used to determine them objectively. The final importance weights were obtained by combining the results of both methods. When evaluating the sustainability performance of Anadolu Sigorta Company, the most important criterion was total paper consumption, while the criterion with the least impact was the number of female employees. The COBRA ranking algorithm was used to rank the alternatives, and it was determined that the company's best sustainability performance was in 2022, while the worst was in 2018. Different sensitivity analyses were used to test the consistency of the proposed model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".