The Complexities and Promise of Standing Beside Indigenous Literacy Scholars: A Language Curriculum Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".