Genetics vs. history: competing explanations of uneven development
Bibliographic record
Abstract
In a 109-page article in the American Economic Review,Quamrul Ashraf and Oded Galor (2013) argue that income inequality between nations is caused by differences in genetic diversity of national populations. Low-income nations have either too much genetic diversity, which hampers trust, or too little, which hinders innovation, both of which are necessary for economic development. A group of anthropologists has dismissed the study for having incorrect data and creating false connections between genetic diversity, trust and innovation. Beyond this, the Ashraf and Galor genetic hypothesis is problematic because it ignores recent epigenetic research and draws a veil over the international economic causes of global inequality. Ashraf and Galor argue that the winners and losers in national incomes have been chosen by nature rather than political and economic history. In tracing the causes of national inequality to genetics, Ashraf and Galor have naturalized history.
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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 | 0.000 |
| 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".