Creating community in digital learning spaces as embodiment of Indigenous self-determination
Bibliographic record
Abstract
This paper discusses Indigenous peoples’ educational experiences with digital technologies in Canada, with references made to Australia and the USA. While COVID-19 has intensified many existing inequities and placed strong focus on abilities to exist in online spaces, Indigenous peoples took to the online world and have been carving out sovereign online spaces for a long time. This article focuses on the ways in which Indigenous peoples have engaged in digital spaces. It provides examples of Indigenous models in these virtual learning spaces, discusses how they draw on Indigenous frameworks, and demonstrates how Indigenous approaches to pedagogy are reflected online. One notable example of this is the revitalization of language. Throughout this paper I provide a reflexive account of my own experiences working in post-secondary contexts where engagement with Indigenous pedagogy has been central to online learning experiences. This paper shows that Indigenous pedagogy and approaches are used effectively to enact self-determination in these digital learning spaces.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.043 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".