Why the Top 5-6 original Oil richest Arab countries as well as some fast moving coginitive skill nations should be treated as outliers in the regression analyses for the relationships between the economic growths vs. the cognitive skill
Bibliographic record
Abstract
In this short paper, I demonstrated that why the top 5 -6 original oil richest countries need to be excluded from most of the socio -economic vs. coginitve skill regressions because they will remain as outliers far too much out to the otherwise very reliable and stable regression growth coefficients and explanation powers of the m odels involved. I included some simple linear regression charts where they are far out in North West corners of the regression lines; their GDP per capita had reached the top tier of the worl d by the 70s already with the minimal cognitive skills and education inputs; I provided their relative economic strength compared to the economic miracle powers from the Eastern Asia: 4 Asian Tigers and China so that you can see their supe r rapid rises were all due to their oil -based economies; their top 6 shares of the Natural R esource rents as percent of capita over the past 40 years. I believe that these 4 key factors may allow anyone serious abo
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".