Engaging Literacies Through Ecologically Minded Curriculum: Educating Teachers About Indigenous Education Through an Ecojustice Education Framework
Bibliographic record
Abstract
In this article, we conceptualize curricula through an EcoJustice Education (EJE) framework to educate teachers about Indigenous and environmental education. The primary tasks of EJE are to engage learners in a cultural analysis of the ecological crisis and in the identification of diverse cultural methods that can bring about eco-democratic reforms that emphasize sustainable ways of living. An important method to infuse Indigenous knowledge into curricula is to invite local Elders to share stories that are Indigenous to place. In this paper, however, we consider methods of developing literacies through an engagement with the places within which learners live. We highlight the importance of developing a relationship with food and place, and an understanding about language through an eco-hermeneutic lens (Kulnieks, Longboat, & Young, 2010; 2011). We demonstrate how an aesthetic teaching form, in this case a poem entitled "Remembering Your Work," can help to foster important connections with local places and the cultural origins of food. Asking students to engage with both oral and literary traditions can promote an important dialogue about intergenerational knowledge, and foster the development of their relationships with food and place. Keywords: EcoJustice Education; Indigenous teachings; intergenerational knowledge; curriculum; literacies
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".