Serving the Stigmatized: Working Within the Incarcerated Environment
Bibliographic record
Abstract
America's incarceration rate was roughly constant from 1925 to 1973, with an average of 110 people behind bars for every 100,000 residents. By 2013, however, the rate of incarceration in state and federal prisons had increased sevenfold to 716. Compared with 102 for Canada, 132 for England andWales, 85 for France, and a paltry 48 in Japan, the United States is the worlds' most aggressive jailer. When one factors in those on parole or probation, the American correctional system is in control of more than 7.3 million Americans, or one in every 31 U.S. adults. This means that 6.7 millionadult men and women - about 3.1 percent of the total U.S. adult population - are now very non-voluntary members of America's "correctional community."Some key questions that need to be addressed are: "What are we doing with those 7.3 million Americans? How are they being treated while they are incarcerated? How can we best prepare them to return to their communities?" More than 650,000 offenders are released back into our communities every year;however, 70% are rearrested within three years of their release. Serving the Stigmatized is the first book of its kind that explores best practices when dealing with a specific prison population while under some form of institutional control. If the established goal of a correctional facility is to"rehabilitate," then it is imperative that the rehabilitation is effective and does not simply serve as a political buzz word. The timing of releasing this book coincides with a real movement in the United States, supported by both conservative and liberal advocates and foundations, to decrease thesize of the prison population by returning more offenders to their communities. The text examines 14 specific populations and how to effectively treat them in order to better serve them and our communities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".