Etuaptmumk - two-eyed seeing: Bringing together land-based learning and online technology to teach Indigenous youth about food
Bibliographic record
Abstract
In 2019 we began an intergenerational Land-based learning program with the goal of engaging a group of Mi’kmaw youth from a rural community in Nova Scotia with their Traditional Foodways. When COVID-19 and the physical distancing restrictions hit Nova Scotia, however, this changed how we implemented the project. We decided to bring youth together virtually and encourage self-directed Land-based learning. This paper describes the dilemmas we faced as we considered what initially seemed like a paradoxical relationship- using online technology to promote Land-based learning. Our aim is to not only draw attention to what we believe to be the centrality of the Land in understanding Indigenous foodways, but also the potential for online technology to enhance youth engagement on and with the Land. We begin by exploring Land-based learning and how it supports teaching about Traditional foodways and Indigenous culture, as well as the challenges and opportunities related to implementing Land-based pedagogy in a virtual environment. Using Etuaptmumk-Two Eyed Seeing as our lens, we argue that online technologies can support Land-based learning, providing principles of local culture are respected and the technology is compatible with community values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".