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Record W2304222919

LIVING IN A "DIFFERENT WORLD": Experiences of Racialized Women in the Criminal Justice System

2009· dissertation· en· W2304222919 on OpenAlexaboutno aff
Jennifer Tasevski

Bibliographic record

VenueQSpace (Queen's University Library) · 2009
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCriminal justiceCriminologyEconomic JusticePolitical scienceSociologyGender studiesLaw
DOInot available

Abstract

fetched live from OpenAlex

The criminalization of women is an area of study that has intrigued many researchers. Using critical race theory, multiracial feminist theory, and radical feminist theory, this research attempts to explain this phenomenon. Through the use of personal interviews with women who are currently reintegrating back into society after being incarcerated, I attempt to uncover the factors which influence female criminality, and analyze the experiences women encounter when confronted by the Canadian criminal justice system. A key hypothesis that fuels this study is that discriminatory practices exist within the Canadian criminal justice system which negatively impact women of colour and Aboriginal women. I argue that the criminalization of women of colour and Aboriginal women occurs as a result of failing to take into consideration the intersectionality of race, class and gender in women who commit criminal acts. This phenomenon occurs due to patriarchal and classist biases that seek to maintain current power structures and relationships by continually oppressing those who do not fit within their group. The findings that emerged from the interviews support my hypothesis and

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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