Doing our work in a good way: a framework of collaboration and a case for Indigenous-only writing classrooms
Bibliographic record
Abstract
Since fall of 2021, UVic has offered a section of the foundational writing course, ATWP 135: Intro to Academic Writing that is dedicated for Indigenous students. This course provides a space for first-year Indigenous students to find a sense of belonging with each other and in the university more broadly, experience anti-oppressive grading practices (Gaudet 2022), develop the confidence to access Indigenous student supports, and navigate the broader institution. In this article, the authors (an Indigenous staff working as the Tri-Faculty Indigenous Resurgence Coordinator and a non-Indigenous faculty serving as the course instructor) discuss the development, delivery, and impact of this initiative. We share an example of promising practice for institutions to consider in the interest of supporting Indigenous student success and retention. In doing so, we also offer a model for collaboration across disciplines and across cultures based on shared values of vulnerability, openness, and honesty.
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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 | 0.039 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.088 | 0.166 |
| Scholarly communication | 0.048 | 0.037 |
| Open science | 0.007 | 0.048 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".