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Record W2989204668

Playing the Blame Game: Indigenous Status and Culpability in Ambiguous Sexual Assaults

2019· article· en· W2989204668 on OpenAlexaffabout
Nicole Wenckowski

Bibliographic record

VenueStudent Research Proceedings · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCulpabilityBlameCriminologyPsychologySocial psychologyIndigenousRace (biology)PopulationCriminal justicePrisonPlaintiffPolitical scienceLawSociologyGender studiesDemography
DOInot available

Abstract

fetched live from OpenAlex

Given the current social and political climate of emphasis on sexual violence awareness, as well as attempts to rectify past injustices that were committed in the ‘name of race’, so to speak, the current study was designed to assess bias within these two relevant areas. Studies concerning racial biases that may be more relevant to Canadian jurors (such as those involving Indigenous persons) are relatively lacking. While there is much statistical and anecdotal evidence to suggest that Indigenous persons are treated differently in the criminal justice system (i.e., prison over-representation, sentencing disparity), there is little evidence specifically focused on biases that may affect jurors in Canadian courtrooms. As a result, the present study was designed to examine whether race and age factors influence decisions concerning criminal culpability. This study was conducted on a University population and consisted of 711 participants. Participants read a transcript of a sexual assault court case that has been adapted from Canadian court records that is an ambiguous case (i.e., unclear whether victim or perpetrator is responsible). Our manipulated variables involve differing descriptions of the complainant (both age and race variations) and defendant (race variations), such that we can examine how these “extralegal” factors may introduce bias in whose testimony is given more weight or deemed more believable. Preliminary analysis shows significant differences in believably, consent, and punishments lengths for false reporting between Caucasian and Indigenous victims. These findings support the idea that racial biases may be relevant to Canadian juries.   Faculty Mentor: Kristine Peace Department: Psychology (Honours)

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.461
Teacher spread0.353 · 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 designObservational
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
Published2019
Admission routes2
Has abstractyes

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