Bibliographic record
Abstract
The Myth and Reality of Poverty in America The United States has the astonishing distinction of having one of the highest rates of poverty in the industrialized world, despite having one of the highest average incomes. The latest figures show an increase in the number of people in poverty from 32.9 million in 2001, to 35.9 million in 2003, to 37 million in 2004. The poverty rate rose from 11.7 percent (2001) to 12.7 percent in 2004. More ominously, the United States also has the highest child poverty compared with thirteen western European countries and Canada. How did we earn such a distinction? The United States believes in the central importance of personal independence through earnings in the paid labor force. Other policies are viewed in terms of whether they create incentives or disincentives to achieving this goal. Thus, with the exception of the aged, only mixed, if not suspicious, support is reserved for those considered outside of the labor force (unemployed, disabled). There is begrudging support for those who are considered poor but able bodied. There has never been public support to combat poverty by reducing income inequality, even though increasing inequality is a major reason poverty is so high in the United States. An exception is the Earned Income Tax Credit (EITC) for the working poor, which is discussed in Chapter 3.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".