Identifying factors of stigma influencing the reintegration of ex-inmates as law-abiding citizens.
Bibliographic record
Abstract
The thesis examines ex-inmates' self-report accounts of their experience with reintegration. In order to arrive at an understanding of this social stage, the labelling approach is used as a broad theoretical framework to develop a typology for understanding the different ways in which stigma is experienced and managed by ex-inmates with different criminal histories and life situations. While the study recognizes the social barriers to reintegration and the problems that every ex-inmate experiences because of social stigma, an important facet of this work is to understand that there are distinct personal, criminal, institutional, and social patterns of the stigma experience and management which can be identified within three ex-offender categories: the career criminal, the occasional criminal, and the episodic or one time offender. The understanding of distinct reintegration needs, based on these categorizations, will assist community corrections in matching ex-offender profiles to the appropriate intensity for community correctional programme delivery, and release criteria to assure that the ex-inmate receives the best fit, based on his profile to become a law-abiding citizen.Dept. of Sociology and Anthropology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .P55. Source: Masters Abstracts International, Volume: 43-01, page: 0110. Adviser: Alan Hall. Thesis (M.A.)--University of Windsor (Canada), 2004.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".