Prison, Re-entry, Reintegration and the ‘Star Gate’: The Experience of Prison Release
Bibliographic record
Abstract
Prison, Re-entry, Reintegration and the 'Star Gate':The Experience of Prison Release Jeffrey Bliss M any people are asking 'why do ex-offenders continue to re-offend or violate the conditions of their release supervisions usually within a 90 day period after release?'But more importantly, many more are saying that this is because individuals 'choose to continue to live the lifestyle of lawlessness, and opt to act and behave in ways that violate the conditions of their release supervision'.However, is it not possible that this type of thinking could not be further from the truth, and that in fact, for many of the criminalized like myself, there could be different reasons altogether?Our nation's recidivism rate has dropped a lot in recent years but it is still holding at approximately 34 percent (Glaze and Bonzcar, 2010).In New York, where I am serving my sentence, of the 24,605 ex-prisoners released between 2011 to 2013, a total of 10,217 (42 percent) of parolees were taken back into custody (Department of Corrections and Community Supervision, 2014).Interestingly, only 9 percent of these men and women were convicted of a new felony, while 32 percent were returned to prison for violating terms of their parole (ibid, 2014).Recidivism rates vary by State, so to give the reader a sense of the magnitude of the problem, consider the following rates of recidivism from jurisdictions who, according to the Council of State Governments Justice Center (2014) have actually lowered their return rates: Colorado (49 percent); Connecticut (40 percent); Georgia (26 percent); North Carolina (28.9 percent); Pennsylvania (40.8 percent); Rhode Island (48.9 percent); South Carolina (27.5 percent); and Wisconsin (51.1 percent).Some States have more disturbing statistics.In Washington State, the recidivism rate in 2007 was 63.3 percent (Sentencing Guidelines Commission, 2008).There are many factors that contribute to the current rate of re-incarceration.Academics and researchers have identifi ed social economic poverty, alcohol and drug addiction, lack of educational/vocational training, mental health disorders, family dysfunction, childhood trauma and/or abuse, and lack of adequate transitional service housing programs and resources, as some of the contributing factors to recidivism.These factors contribute not only to the small number of parolees who commit new crimes, but also to the thousands who return for technical violations of their parole.For example, of the 24,520 men and women paroled in New York in 2008, 29 percent had their parole revoked and were returned to prison.Twenty-three percent of the time these
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".