Ranking industries using a hybrid of DEA-TOPSIS
Bibliographic record
Abstract
Ranking industry normally helps find hot sectors and attract potential investors to invest in appropriate plans. Ranking various industries is also a multiple criteria decision making problem. In this paper, we present an empirical investigation to rank different industries using the art of data envelopment analysis (DEA). The inputs of our proposed DEA model include capital, employment and importance coefficient and outputs are exports, ecological effects and added value. In addition, exports, value added and environmental investment are used as outputs of DEA method. Since the results of DEA may consider more than one efficient unit, so we implement Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique to rank efficient units. In our case study, there were 15 different sectors from various industries and the implementation of DEA technique recommends 8 efficient units. The implementation of TOPSIS among these efficient units has suggested that Chemical industry could be considered as the most attracting industry for investment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".