Theorizing and implementing meaningful Indigenization: Wikipedia as an opportunity for course-based digital advocacy
Bibliographic record
Abstract
This article is inspired by long-standing calls to address issues of anti-Indigenous racism and colonialism within higher education. There is a growing trend among universities around the globe to commit to principles of equity, diversity, and inclusion (EDI), including discussions about how to Indigenize the academy. While EDI and Indigenization goals are laudable, they are often critiqued as superficial policies that fail to disrupt the status quo of everyday racism and colonialism embedded within academic institutions. In response, we contend that scholars must carefully think through the concept of Indigenization guided by critical Indigenous theories to ensure meaningful application over performative inaction. Critical Indigenous theory grounds our analysis and reflections of using Wikipedia in the higher education classroom. We illustrate how Wikipedia can be used in the classroom as a site of digital advocacy to foster meaningful and sustainable change that aligns with the tenets of critical Indigenous theories, such as Indigenous storywork, resisting damage, and resurgence-based decolonial Indigenization. Our contribution showcases how implementing Wikipedia is one pedagogical strategy that can be implemented to challenge the status quo of knowledge production within and beyond academia.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| 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".