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Record W3137693939 · doi:10.18357/ijcyfs121202120084

“HONOUR”- BASED VIOLENCE AND THE POLITICS OF CULTURE IN CANADA: ADVANCING A CULTURAL ANALYSIS OF MULTI-SCALAR VIOLENCE

2021· article· en· W3137693939 on OpenAlexaffvenueabout
Salina Abji, Anna C. Korteweg

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of TorontoArtificial Intelligence in Medicine (Canada)College of Family Physicians of Canada
Fundersnot available
KeywordsHonourSociologyPoliticsSituatedCritical discourse analysisGender studiesCriminologyPolitical scienceLawIdeology

Abstract

fetched live from OpenAlex

Since 2015, in Canada, political discourse on “honour”-based violence has shifted away from highly problematic understandings of “culture” as the cause of violence among racialized, Muslim, and immigrant communities. Instead, talk of culture has dropped out of the equation altogether in favour of more structural definitions of gender-based violence (GBV). In this article, we ask what gets lost when culture is not taken into account when talking about or trying to understand forms of GBV. Drawing from theoretical conceptualizations of culture — defined as “situated practices of meaning-making” that shape all experiences of violence, and societal responses to violence — we argue for a multiscalar approach to culture. To illustrate this framework, we first offer a critical analysis of Aruna Papp’s 2012 memoir Unworthy Creature as an exemplar of stigmatizing uses of culture and a key text promoted by the Conservative federal government at the time. We then turn to interviews we conducted with service providers serving South Asian survivors of GBV in Toronto from 2011 to 2013. Our analysis illustrates how to talk about culture as a key ingredient shaping multiscalar violence, regardless of whether that violence occurs in majority or minority communities. We conclude with three policy implications for addressing HBV moving forward.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.320
Teacher spread0.294 · 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

Citations9
Published2021
Admission routes3
Has abstractyes

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