MétaCan
Menu
Back to cohort
Record W1832822507 · doi:10.37119/ojs2011.v17i3.71

Disrupting Ignorance and Settler Identities: The Challenges of Preparing Beginning Teachers for Treaty Education

2013· article· en· W1832822507 on OpenAlexafffundvenueabout
Jennifer Tupper

Bibliographic record

Venuein education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsUniversity of Regina
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Regina
KeywordsTreatyIgnoranceGovernment (linguistics)CurriculumPolitical scienceSociologyTreaty of WaitangiWhite (mutation)LawPublic administration

Abstract

fetched live from OpenAlex

In the fall of 2008, the Provincial Government of Saskatchewan announced mandatory treaty education for all students in K-12 schooling. Given the foundational importance of treaties and the treaty relationship to Canada, and ongoing reconciliation efforts with First Nations people, this initiative is to be celebrated. However, a central concern exists regarding the implementation of treaty education in Saskatchewan schools. To that end, this paper discusses research, with 348 predominately white, teacher education candidates at the University of Regina, regarding their knowledge, (mis)understandings, and experiences with treaty education, in both grade school and university contexts. Using critical race theory as a lens through which to conceptualize and make sense of the research, along with theories of ignorance as an epistemological exercise, the paper illustrates the imperative of enacting treaty education given (white) settler students struggles(and refusals) to connect their own social and economic privileges to treaties.Keywords: Treaty education; critical race theory; curriculum

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)0.010
metaresearch head score (Gemma)0.015
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 score0.058
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0300.025
Scholarly communication0.0110.006
Open science0.0010.010
Research integrity0.0020.008
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 head0.022
GPT teacher head0.385
Teacher spread0.363 · 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

Citations62
Published2013
Admission routes4
Has abstractyes

Explore more

Same venuein educationSame topicCritical Race Theory in EducationFrench-language works237,207