Analysis of the Increasing Income Gap between the Rich and Everyone Else
Bibliographic record
Abstract
The growing disparity in income between the rich and middle/lower income groups has resulted in significant skewness in the distribution of wealth in the U.S. The short and tall of it is that the real incomes of the top .01 percent of Americans rose seven fold between 1980 and 2007, but the real income of the median family rose only 22 percent, less than a third of its growth over the previous 27 years. Two main reasons given by economists are technological innovation and inadequate technical education in the U.S. However, there are several other factors that are relevant. This paper analyzes the other issues: 1) the funding of federal political campaigns, 2) the effects of offshoring, 3) the role of the U.S. tax code, and 4) the absence of a strong connection between performance and rewards that may be related to the recent shift in wealth. While wage differences naturally occur in a capitalistic system, massive differences provoke social unrest and the rise of demigods advocating collectivist solutions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".