Steps on the Path towards Decolonization: A Reflection on Learning, Experience, and Practice in Academic Support at the University of Manitoba
Bibliographic record
Abstract
This essay comes out of a panel presentation featured at the 2018 Canadian Association Writing Centres Conference entitled, “Steps on the Path of Decolonization” where representatives of the Academic Learning Centre and the Indigenous Student Centre from the University of Manitoba collaborated to discuss how our student support offices have made efforts at decolonizing our work. We three women of Canadian Settler, International, and Indigenous backgrounds reflect on how the journey on the path towards decolonization has been for us personally, and on how post-secondary institutions can move forward with the work of decolonization, particularly within Writing/Learning Centres. Key themes included the need for ongoing learning, the value of building collaborative relationships, and the importance of creating safe and inviting spaces. Our conclusions suggest that decolonization is a complex journey for individuals and for post-secondary institutions. To begin in a good way, the writers would like to acknowledge that we work at the University of Manitoba, which is located on original lands of Anishinaabeg, Cree, Oji-Cree, Dakota, and Dene Peoples, and on the homeland of the Métis Nation.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.085 | 0.050 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".