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Examining Racism and Settler-Colonialism in Canadian Social Work Education

2024· book-chapter· en· W4403633718 on OpenAlexaffabout
Stephanie Tyler, Sheliza Ladhani

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsColonialismRacismSociologyGender studiesWork (physics)Political scienceCriminologyEngineeringLaw

Abstract

fetched live from OpenAlex

Abstract This chapter examines racism and settler-colonialism in Canadian social work education and the potential to (re)shape the contours of curriculum and pedagogy toward the aims of antiracism and decolonization through engaging decolonizing pedagogies and pedagogies of discomfort that work effectively with difficult knowledges. The chapter situates Canadian higher education within settler-colonialism and the recent global events that have compelled universities to issue statements of solidarity with equity-deserving groups. It then traces the complicity of Canadian social work education with colonial projects through a review of administration, accreditation, curriculum, and pedagogy, while attuning to the distinct realities and challenges of racialized and Indigenous educators and students. Finally, the chapter considers how difficult knowledges might be more ethically engaged in social work education through embodied pedagogies, while recognizing the impossibilities of teaching and learning and surrendering to the reality of teaching without guarantee.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.661

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0150.010
Scholarly communication0.0070.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.044
GPT teacher head0.286
Teacher spread0.243 · 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.

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

Citations0
Published2024
Admission routes2
Has abstractyes

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