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Record W2965667242 · doi:10.24908/jcri.v6i1.6997

Whose values, who's valued?

2019· article· en· W2965667242 on OpenAlexaffvenueabout
rosalind hampton, Michelle Hartman

Bibliographic record

VenueJournal of Critical Race Inquiry · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRace, History, and American Society
Canadian institutionsMcGill University
Fundersnot available
KeywordsRacializationNarrativeContext (archaeology)Gender studiesSociologyCriminologyHistoryRace (biology)ArtLiterature

Abstract

fetched live from OpenAlex

The first week of February 2014 saw the tragic deaths of two young people in Québec: Naïma Rharouity, a Muslim woman and mother of two who died following an accident in a metro station and Alain Magloire, a Black man and father of two killed by the Montreal police. Muslim women and Black men are racialized within Québec society in significantly different ways from one another, in life as in death. This article analyzes the reactions to and representations of these two deaths in the specific context of Québec and how they fit into heavily racialized scripts. A Muslim woman is strangled to death when an escalator catches her clothing; her hijab is blamed, making her a victim of her culture and dead because of the scarf she wore on her head. A Black man holding a hammer outside a metro station is deemed as so dangerous, violent, and threatening by armed police officers that he is shot dead. Both were victimized but also blamed for their untimely deaths. The challenges these stories pose disrupt assumptions and demand alternative narratives about racialized bodies. This article reveals the different processes of racialization of Muslim women and Black men, and argues that exposing the internal logic of this comparison promotes critical understanding of the ways in which racialized scripts shape and influence our lives. This further highlights ways to work towards building stronger solidarities to resist and challenge narratives that demand tragic endings for racialized bodies.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.373
Teacher spread0.342 · 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.

Study designNot applicable
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

Citations2
Published2019
Admission routes3
Has abstractyes

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