Bibliographic record
Abstract
Background: Indigenous learners and community members are often excluded from online learning environments, as both consumers and producers of knowledge, resulting in an educational digital divide. Further, Indigenous knowledges represented through digital practices and online spaces risk misrepresentation and appropriation, which leads to stereotypes and deficit thinking about Indigenous people, their histories, and their current realities. There is a need for educational approaches that give space, voice, and agency to Indigenous people. Aim: This article is a reflection on a teaching enhancement project that weaved together local land-based learning, Indigenous storytelling, and digital media. Project Overview: Indigenous pre-service teachers created an open educational resource, the Grease Trail Digital Storytelling Project, to enhance the preservation and accessibility of Indigenous histories, stories, and memories embedded in local landscapes. Their approach to Indigenous digital storytelling uses the principles of respect, relevance, responsibility, and reciprocity to document and curate their digital storytelling practices and Indigenous knowledgetraditions. Discussion: The Grease Trail Digital Storytelling Project may serve as a helpful resource for those interested in learning how Indigenous digital storytelling could be approached for the preservation of Indigenous intellectual traditions that bring together land, story, and memory in online spaces and integrated as a tool for teaching and learning in school and community settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".