Can we really teach ‘Indigenizing’ courses online?
Bibliographic record
Abstract
On April 16, Canadians -and internet users around the world -have the opportunity to participate in "Indigenous Canada," a Massive Open Online Course (MOOC) offered through the University of Alberta and the Coursera consortium of online learning providers.Similar courses -for instance on "Reconciliation through Indigenous education" and "Aboriginal worldviews and education" -are offered by the University of British Columbia and the University of Toronto respectively."Indigenization" of the curriculum is an urgent issue in Canadian higher education.As a non-Indigenous faculty member at Thompson Rivers University, I make no claims to speak for, or about, Indigenous communities.I specialize in learning technologies, have a strong interest in instructional design and e-learning and my doctoral thesis was on social presence in MOOCs.My university has made a priority of "Indigenizing" curricula and expressed the desire to be a "university of choice" both for Indigenous students and for open, distance and online education.Given my role as a faculty member is to support learning technologies and instructional design initiatives, I have taken a strong interest in Indigenization programs across Canada. Indigenous ways of knowingSo what are we talking about when we discuss Indigenization? Camosun College, in their "Inspiring relationships" strategic document provides a useful starting point in this discussion: "Indigenization is the process by wh ich Indigenous ways of knowing, being, doing and relating are incor porated into educational, or ganizational, cultural and social structures."Can we really teach 'Indigenizing' courses online?
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.163 | 0.032 |
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".