Novel Liberalism Coefficient to Improve Human Freedom Development in the World
Bibliographic record
Abstract
The new concept to measure human freedom examines the relationship of the Coefficient of Liberalism (L); and, the variables grouped in three dimensions: the forces of modern markets, private property; and, Institutionality.The analyzed population corresponded to 116 countries.158 variables were collected per country for 10 years.For the analysis, information from The Global Competitiveness Index Historical Dataset © 2008-2018 of the World Economic Forum was used, with which an Xnxl Database was used with index and coefficient values, country codes, global id, identified series and treatments (Income groups, Regions and Forum classification).The hypothesis test, linear regression analysis, ANOVA, PCA, univariate variance and eta-square were used as statistics.The L Coefficient has a statistically significant positive correlation with the Global Competitiveness Index (R 2 =0,82; F(1,114) = 516,61; Sig.=,000)); and, it served to evaluate the three treatments analyzed.The means of the income groups differ significantly, F(1,112) = 5,68, p < ,001, η2 = 0,14 for the dependent variable of the Coefficient of Liberalism (L).In addition, the means of the Regions differ significantly, F(1,109) = 2,77, p < 001, η2 = 0,14.The squared Eta value indicated a large effect of income groups and Regions on the L Coefficient.The five countries with the highest L Coefficient were United States (14,56), Hong Kong SAR (12,63), Singapore (12,60), Canada (12,28); and Germany (12,23).This analysis confirmed the power of the L Coefficient to identify the countries that maximize human freedom.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".