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Record W2029798638 · doi:10.15353/cgjsc.v3i1.3757

Canada’s Mother-Child Program: Examining Its Emergence, Usage, and Current State

2014· article· en· W2029798638 on OpenAlexaffvenueabout
Sarah Brennan

Bibliographic record

VenueCanadian Graduate Journal of Sociology and Criminology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOvercrowdingPunitive damagesPsychologyState (computer science)Political sciencePublic relationsMedicineLaw

Abstract

fetched live from OpenAlex

One of many issues a mother must face while incarcerated is separation from her child(ren) for an extended period of time. Empirical findings have consistently highlighted various negative effects for both mothers and their children as a result of this separation. To curb some of the negative effects, Correctional Service Canada’s Mother-Child Program offers full- and part-time visitation between children and their incarcerated mothers at various women’s federal correctional facilities in Canada. The current study involves an in-depth critical analysis of Canada’s MCP by asking three related questions. First, to what extent has the MCP been used since its full implementation in 2001? Second, to what extent is the MCP used today? Third, do any barriers exist currently that are inhibiting the success of the MCP and, if so, how can these be addressed? The results of the study reveal that, since the full implementation of the program in 2001, the participation rate declined from an already low starting point and has remained relatively low since. Further, three main factors were suggested as potential barriers impeding the success of the MCP: correctional overcrowding, a more punitive institutional culture, and a series of changes to the program’s eligibility criteria. Recommendations on ways to increase the usage of the program are offered and suggestions for future research are made.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.327
Teacher spread0.219 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations14
Published2014
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Graduate Journal of Sociology and CriminologySame topicCriminal Justice and Corrections AnalysisFrench-language works237,207