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Record W2900575703

Attitudes of suspicion, perceptions of criminality and willingness to report Muslims in Canada

2017· dissertation· en· W2900575703 on OpenAlexaboutno aff
Maaha Farrukh

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionPsychologyCriminologySocial psychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The current research incorporated two studies to examine stigma toward Muslims within a forensic psychology framework. The studies utilized fictional news coverage to assess participant???s attitudes of suspicion, perceptions of criminality and willingness to report Muslims vs. control suspects. In Study 1, 216 students from a Canadian university read about a suspicious event involving Muslims vs. control, conducted by Muslim and non-Muslim experimenters. In Study 2, 192 students read about either a burglary (home invasion) or explosion (terrorism) crime scenario, where the identity of the ???person of interest??? was either Muslim or Caucasian, conducted by either Muslim or non-Muslim experimenters. Results of Study 1 indicate greater suspicion toward suspects with Muslim experimenters, whereas Study 2 demonstrated greater suspicion, perceptions of criminality and willingness to report the Caucasian person of interest. The findings are discussed in terms of aversive racism and the implications for Canadian Muslims in forensic settings.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.281
Teacher spread0.263 · 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 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
Published2017
Admission routes1
Has abstractyes

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