Years of life lost to prison: racial and gender gradients in the United States of America
Bibliographic record
Abstract
BACKGROUND: The United States has the highest rate of imprisonment of any country in the world. African Americans and Hispanics comprise a disproportionately large share of the prison population. We applied a "prison life expectancy" to specify differences in exposure to imprisonment by gender and race at the population level. METHODS: The impact of imprisonment on life expectancy in the United States was measured for each year from 2000 to 2004, and then averaged. Using the Sullivan method, prison and prison-free life expectancies were estimated by dividing the years lived in each age range of the life table into these two states using prevalence of imprisonment by gender and race. RESULTS: African American males can expect to spend on average 3.09 years in prison or jail over their lifetime and Hispanic and Caucasian males can spend on average 1.06 and 0.50 years, respectively. African American females, on the other hand, can expect to spend on average 0.23 years in these institutions and Hispanic and Caucasian females can expect to spend on average 0.09 and 0.05 years, respectively. Overall, African American males, the highest risk group, can expect to spend on average 61.80 times longer in prison or jail as compared to Caucasian women, the lowest risk group. CONCLUSION: There are clear gender and racial gradients in life expectancy spent in prison in the United States. Future research needs to examine how current imprisonment practice in the United States may influence population health and health disparities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".