Bibliographic record
Abstract
Abstract In reviewing Indigenous approaches to open, distance, and digital education, the authors found that Indigenous people have been keen to adopt and adapt technologies for their own uses and purposes but are less successful in controlling and creating technologies that dominate the learning landscape. Given the scant literature available on this topic, using the methodologies of kitchen table talks, the authors dialogue their experiences working with Indigenous people and designs in open, distance, and online teaching and education. Through their storytelling, the authors elicit examples of experience in postsecondary education contexts in Canada including the use of talking circles, blended and inclusive learning, development of safe spaces and hubs, and challenges balancing home life and online learning. The importance of relationships, community connection, and validating self and identity in the learning experience were strong themes that emerged from the dialogue. Indigenous pedagogies and knowledges online is a relatively unexplored phenomenon and this initial foray into characteristics, successes, and challenges may be a starting point for future scholars to follow. By sharing highly contextualized narratives from Canada, we aim to increase the global dialogue around decolonizing ODDE and therefore end the chapter by examining our experience against ongoing international discussions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".