Neither “Post-War” Nor Post-Pregnancy Paranoia: How America’s War on Drugs Continues to Perpetuate Disparate Incarceration Outcomes For Pregnant, Substance-Involved Offenders
Bibliographic record
Abstract
This thesis investigates the unique interactions between pregnancy, substance involvement, and race as they relate to the War on Drugs and the hyper-incarceration of women. Using ordinary least square regression analyses and data from the Bureau of Justice Statistics’ 2016 Survey of Prison Inmates, I examine if (and how) pregnancy status, drug use, race, and their interactions influence two length of incarceration outcomes: sentence length and amount of time spent in jail between arrest and imprisonment. The results collectively indicate that pregnancy decreases length of incarceration outcomes for those offenders who are not substance-involved but not evenhandedly -- benefitting white pregnant offenders more than their Black counterparts. Similarly, any incarceration length leniency resulting from pregnancy does not apply uniformly once substance involvement is factored in: while pregnant, white, substance-involved offenders spend less time incarcerated than their nonpregnant, non-substance-involved white counterparts, they often received longer incarceration outcomes than those who were pregnant, white, and not substance-involved. The analyses reveal similar patterns among Black offenders, but the sentencing disparities associated with pregnancy and substance involvement are magnified: the results indicate that not only does substance involvement increase incarceration length among pregnant Black offenders, but several model specifications demonstrated that Black offenders who are both pregnant and substance-involved receive harsher sentencing outcomes and more jail time than their nonpregnant, non-substance-involved Black counterparts. These findings indicate that, despite a public departure from its most attention-grabbing components, the War on Drugs has contributed to a carceral system that disproportionately harms women -- especially Black women -- who are substance-involved and pregnant. The concluding analysis of my results underscores the unique intersections between the criminal justice and public health crises created by this “war” and implications for the populations most affected.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".