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Record W7006508449

UNDERSTANDING THE LEGISLATIVE PROCESS OF STRUCTURED INTERVENTION UNITS – CAN STRUCTURED INTERVENTION UNITS SUCESSFULLY TRANSFER FROM LAW INTO PRACTICE?

2022· dissertation· en· W7006508449 on OpenAlexaboutno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2022
Typedissertation
Languageen
FieldImmunology and Microbiology
TopicAlexander von Humboldt Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutionalityLegislatureVettingGovernment (linguistics)LegislationStakeholderIntervention (counseling)Process (computing)Psychological intervention
DOInot available

Abstract

fetched live from OpenAlex

Research indicates through perceptual and sensory deprivation as well as social isolation, including restrictions on a prisoner’s freedom of association, assembly and movement, solitary confinement leads to the creation, maintenance, and aggravation of mental and physical harms. Several legal challenges have been launched challenging the constitutionality of using solitary confinement, with two major court decisions in Ontario and British Columbia rendering solitary confinement unconstitutional. The Canadian government responded by introducing Structured Intervention Units (SIUs) through Bill C-83, eliminating solitary confinement in federal prisons. This thesis seeks to determine if SIUs can successfully transfer from policy into practice through analyzing the legislative process of SIUs. By performing a qualitative content analysis of various publicly available documents such as judicial decisions, House of Commons Debates, Senate Debates, their respective committees and stakeholder submissions three themes emerge. Meaningful contact, length of placement and oversight mechanisms are the themes which have been utilized to demonstrate the difficulty of successfully implementing SIUs at the institutional level. This work will set the stage for future research to examine the long-term impacts this policy change will have on those most affected. To determine if this is the best we can do or if more needs to be done to ensure offenders and frontline staff are provided with the proper tools and resources to successfully benefit from this new practice. Furthermore, this research is both important and timely to ensure the same harms evident with administrative segregation are not replicated under the new regime of SIUs.

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)0.115
metaresearch head score (Gemma)0.159
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.609

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1150.159
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0190.073
Scholarly communication0.0220.037
Open science0.0050.009
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.021
GPT teacher head0.240
Teacher spread0.218 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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