Exponential expansions in social evolution: the case of per capita income averages in the U.S.
Bibliographic record
Abstract
The exponential expansion of per capita incomes over the last centuries in tech driven economies like that of the U.S. is the most recent in a long series of social creations marked by one or another exponential expansion. The origins of the deep attraction to these enhancements are found in the evolutionary heritage of our expanded cognitive and affective capacities. These evolved capacities have always marked the exponentially rising learning curve in the rapid language acquisition of young humans. They have also played a role in the emergence of religion as an explanation scaffolding from our context of origin onwards. After the exodus from our context of origin into larger and larger populations, they helped shape the emergence of the inflated ascriptive inequality that grew as individuals struggled to find a framework to make and assess status claims at these larger social scales. Among many other changes, the exponential expansion of per capita income averages over the last centuries in economies that first embraced systematic technological innovation and diffusion provided an alternative material enhancement for status differentiation throughout a population. The contrasting labor-saving and disruption costs of this fourth enhancement have played a role in the remarkably steady pattern of a multi-century per capita growth rate average in the U.S. of just under 2% per year.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".