Bibliographic record
Abstract
The rate of incarceration has increased dramatically in the U.S. since 1980. This is attributable to a higher rate of sentencing per crime committed and to an increased prevalence of drug-related crime. We explore the implications of this increased incarceration on national poverty measurement using micro data for the period 1979--1997. We make use of an as-yet unexplored data set on prisoner earnings, in conjunction with the Luxembourg Income Surveys to compute earnings of the whole population. Sen's generalized measure of poverty is the poverty index we choose. This index encompasses the rate of poverty, the income gap of the poor, and the degree of inequality among the poor. It is found that the traditional measurement of poverty, which omits this increased share of the population that has become institutionalized, understates the true degree of poverty in the nineteen nineties to a significant degree. This underestimation has increased during the time period of study. Furthermore, it is the depth of poverty associated with the higher incarceration rate, rather than the higher rate of incarceration alone that has had the greatest impact upon poverty. These results stand in marked contrast to western European economies and Canada, which have not experienced such increases and whose levels of incarceration are a fraction of the U.S. level.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".