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Record W4313169057 · doi:10.1177/14704129221088300

Rupture, not injury: reframing repair for Black and Indigenous youth experiencing school pushout

2022· article· en· W4313169057 on OpenAlexaboutno aff
Jade Nixon, Sefanit Habtom, Eve Tuck

Bibliographic record

VenueJournal of Visual Culture · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive reframingRacismIndigenousGender studiesSociologyParticipatory action researchPsychologySocial psychologyAnthropology

Abstract

fetched live from OpenAlex

In this article, the authors describe their multi-year youth participatory action research project, Making Sense of Movements (MSOM), with Black and Indigenous high school students in Toronto. Youth co-researchers in MSOM designed a study on school pushout that reveals the pervasiveness of racism in schools and the inadequacy of responses to racist incidents by school personnel. School staff and teachers often treat racist incidents as isolated events that can be easily resolved. However, the authors situate Black and Indigenous students’ experiences of racism in their high schools within the ongoing legacies of settlement and slavery. Learning from Black and Indigenous feminist theories of rupture and refusal – see Hartman’s Scenes of Subjection: Terror, Slavery, and Self-Making in Nineteenth-Century America (1997); Simpson’s Mohawk Interruptus: Political Life across the Borders of Settler States (2014); and Tuck and Yang’s ‘Decolonization is not a metaphor’ (2012) – the authors invite readers to reframe the assumed ease and completeness of repair. They theorize racism and antiblackness as a rupture rather than an injury, which has important implications for school policy and how schools address racism. By moving beyond reparative frameworks, the authors engage rupture as a more meaningful starting place.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0280.025
Scholarly communication0.0090.008
Open science0.0030.019
Research integrity0.0050.009
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 head0.028
GPT teacher head0.402
Teacher spread0.375 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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