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Record W4296205776 · doi:10.1080/10511253.2022.2121001

“A Lot of my Day is Spent Looking at Injustice”: Reflections on Decolonizing Teaching and Relationship Building across Difference

2022· article· en· W4296205776 on OpenAlexaff
Michaela McGuire, Danielle J. Murdoch

Bibliographic record

VenueJournal of Criminal Justice Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDecolonizationSociologyScholarshipPedagogyIndigenousSyllabusThematic analysisColonialismDisappointmentGender studiesQualitative researchPsychologySocial psychologySocial sciencePolitical sciencePolitics

Abstract

fetched live from OpenAlex

Working toward decolonizing pedagogy involves discomfort, as one pushes back against the status quo, challenging colonial embeddedness in education and meaningfully integrating Indigenous literature and scholarship. In this study, we conducted a thematic analysis of our respective reflections written throughout our involvement in a post-secondary decolonizing pedagogy and syllabi project. This analysis demonstrates our unique positionalities and the importance of working together across difference. We situate our analysis in a discussion of decolonization and consider what it means to be an ally and an accomplice. Then, we present and reflect upon the two emergent themes (1) relationality and (2) emotions, and the subthemes of: (a) frustration, fear, and discomfort; (b) disappointment; (c) time and energy required to do this work; and (d) hope. We conclude by sharing “takeaways” for others to reflect upon as they pursue their own decolonizing curricula efforts.

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)0.011
metaresearch head score (Gemma)0.024
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 score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0280.035
Scholarly communication0.0070.006
Open science0.0030.011
Research integrity0.0040.014
Insufficient payload (model declined to judge)0.0030.001

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 head0.087
GPT teacher head0.475
Teacher spread0.389 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueJournal of Criminal Justice EducationSame topicCritical Race Theory in EducationFrench-language works237,207