MétaCan
Menu
Back to cohort
Record W2560278541 · doi:10.20381/ruor-19045

After prison: Pathways to reintegration for older women in Ottawa

2008· dissertation· en· W2560278541 on OpenAlexaboutno aff
Laura Shantz

Bibliographic record

VenueuO Research (University of Ottawa) · 2008
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPrisonGerontologyCriminologyPsychologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

As the population ages, the number of older adults who are involved in the criminal justice system is increasing. While their experiences in prisons have been explored, older women's lives in their communities have yet to be studied. This thesis examines the reintegration experiences of older female ex-prisoners living in Ottawa through the perspectives of the professionals who assist them in their reintegrations. Using standpoint theory, I conducted semi-structured interviews with a variety of professionals who described their experiences working with older reintegrating women for non-governmental community organizations in Ottawa. Participants examined various aspects of the reintegration experience, including the communities in which older women live; their health; the social networks surrounding older women; roadblocks which create difficulties during reintegration; and what older women require in order to reintegrate successfully. Through participants' accounts, I describe the challenges and opportunities older reintegrating women face and explore what can be done to ensure that they have the best possible reintegration experiences.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.674

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.005
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.047
GPT teacher head0.345
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueuO Research (University of Ottawa)Same topicCriminal Justice and Corrections AnalysisFrench-language works237,207