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Record W4399299225 · doi:10.1177/0092055x241248185

Unsettling Sociology Curriculum: Indigenous Content in Introductory Sociology Textbooks

2024· article· en· W4399299225 on OpenAlexaffabout
Kathleen Rodgers, Willow Scobie

Bibliographic record

VenueTeaching Sociology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsUniversity of OttawaUniversity of the Fraser Valley
Fundersnot available
KeywordsSociologyIndigenousCurriculumSociology of EducationContent (measure theory)Social sciencePedagogyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Teaching introductory sociology is one of the primary means by which sociologists mobilize knowledge. Ongoing critical reflection on the content of sociology textbooks is therefore an important disciplinary enterprise. The current critical moment in which many nations, institutions, and publics face a reckoning with their historic and current relationships with Indigenous peoples presents sociologists with the opportunity to examine how Indigenous peoples, histories, and perspectives are to be found in these pedagogical materials. Drawing on Critical Indigenous scholarship that “disrupts the certainty of disciplinary knowledges[’]” concept of “connected sociologies,” we examine the state of inclusion of Indigenous content in introductory sociology curriculum. To achieve this, we conducted a content analysis of 10 of the top-selling English-language Canadian introductory sociology textbooks, and we drew directly from interviews with Indigenous scholars. By introducing the literature on solidarity and allyship in the final section, we conclude with teaching and learning actions to incorporate in sociology courses.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.180
Threshold uncertainty score0.358

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.129
GPT teacher head0.428
Teacher spread0.299 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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