Socio-Econo-Engineering: What is the Right Dose of Capitalism Regarding Fertility? Recommendations How to Use Capitalism for Population Control and How to Avert the Falling Rate of Fertility in Capitalist Territories
Bibliographic record
Abstract
Based on a 180 country strong worldwide data set and cross sectional correlation studies, this paper outlines that hallmark pillars of capitalism are all negatively associated with fertility rates. The 2017 Economic Freedom Index is significantly negatively correlated with fertility rates around the globe. Based on a 139 country strong worldwide dataset on industrialization as measured by the UNIDO in the Industrialization Intensity Index of 2014 and fertility rates, a highly significant negative relation is found between industrialization and fertility rates around the world. Urban areas around the world tend to have higher fertility rates and access to markets within rural communities lowers fertility rates measured by the World Bank Rural Access Index for 64 countries around the world. The inverse relation of economic freedom and fertility was also found for 50 U.S. states based on the 2017 Economic Freedom Index and fertility rates in the United States.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".