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Record W4408345289 · doi:10.22329/jtl.v19i1.8991

The Complexities and Promise of Standing Beside Indigenous Literacy Scholars: A Language Curriculum Analysis

2025· article· en· W4408345289 on OpenAlexaffvenueabout
Katie Brubacher, Jacqueline Filipek

Bibliographic record

VenueJournal of Teaching and Learning · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIndigenousCurriculumLiteracyMathematics educationPedagogySociologyPsychologyBiology

Abstract

fetched live from OpenAlex

Literacy is an essential component of any elementary-school classroom. To address shifting understandings of literacy and how to teach it, Alberta has developed a new language-arts curriculum. This curriculum, however, was developed in a context where schools have a long history of not serving Indigenous children well, including not meeting their needs through literacy programs (Hare, 2011). Alberta Education, through the English Language Arts and Literature (ELAL) curriculum, claims to better address those needs. The purpose of this research is to examine how the ELAL curriculum and its implementation aligns with the field of language and literacy, and in particular, Indigenous literacy scholarship, namely Peltier’s (2016/2017) Wholistic Anishinaabe Pedagogy and Reese’s (2018) Critical Indigenous Literacy. Data included both an analysis of the curriculum and semi-structured interviews with literacy instructors/scholars and in-service teachers. There were several key findings: English only processes, sparce attention to feelings throughout the curriculum, an absence of critical literacy, and inappropriate text selection. This paper is significant, as it shows the complexities and promise of being a non-Indigenous literacy scholar, thinking deeply about places of resonance and tension in literacy in ways that Indigenous scholars are already writing about.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.276
Teacher spread0.268 · 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 teacher head, 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
Published2025
Admission routes3
Has abstractyes

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