FUZZY LINGUISTIC MODELING OF EASE OF DOING BUSINESS INDICATORS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ease of Doing Business (EDB) indicators are essential to overall understanding and evaluation of national business environment, and strategy formulation for business policy and regulations. The World Bank does an annual study of these indicators for over 170 nations, but there are many complications and uncertainties involved in the work. This paper proposes a new systematic approach that employs fuzzy set theory to generate composite EDB indicators for ranking and classification problems. We implemented this approach and illustrate its steps and procedures. A case study example for Canada is also presented in which EDB indicators are evaluated, linguistically identified, and ranked. This approach demonstrates the ease of using this fuzzy application, and its potential benefits for future research. We also compare ranking results, obtained from our proposed approach, with the World Bank's results.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it