Grey relational analysis of country-level entrepreneurial environment: A study of selected forty-eight countries
Bibliographic record
Abstract
The aim of this study is to evaluate the entrepreneurial environment of selected countries, and analyze and rank them on the basis of entrepreneurship related indicators. This study’s design comprises a review of the literature, extraction of secondary data on the phenomenon, and analysis. The research gap has been established through a review of the literature, leading toward the development of problem statement. The cross-sectional data, related to entrepreneurship indicators, is extracted from website of World Development Indicators (2021) for 48 selected countries. Using positivism as a research philosophy and deduction as a research approach, the data are analyzed through grey relational analysis (GRA). On the basis of grey relational grades, this study also classified the countries on the continuum of “much better” to “worse.” The results of the study show that the United States, United Kingdom, Japan, Australia, Hong Kong SAR, China, France and Canada attained the highest grey relational grades and are considered the countries having much better entrepreneurial environment; whereas Poland, Kuwait, Namibia and so on attained the lowest grey relational grades and are considered the countries having worst entrepreneurial environment. This research has several practical implications for different economies/countries, entrepreneurial ventures, aspiring entrepreneurial, and researchers. On the basis of findings of this study, policy makers should refine country-level entrepreneurial policies while keeping in view the respective grey relational grades.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".