Bibliographic record
Abstract
Economic output is shown to be related to population (N) and natural resources (R) by a simple power law.On the basis of the exponents for N and R, called, respectively, the "ingenuity index" (n) and the "technology index" (r), the regions of the world fall into three clusters: high n and high r (Western and Eastern Europe, South America, Australia, New Zealand, the USA and Canada); high n and low r (the USA, the Middle East); and low n and low r (Asia, Africa).Even the highest values (of n) barely exceed unity, however.n was found to be wellcorrelated with other independently obtained exponents characteristic of human ingenuity, such as those governing the number of telephone lines, patents, and the diversity of occupations.The analysis of r reveals that there are two kinds of capital: natural resources and technology, especially information technology.However, endogenous productivity-depressing factors appear to impose intrinsic limits on what ingenuity and technology can achieve.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".