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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".