Decolonizing the University: the Challenges and Possibilities of Inclusive Education
Bibliographic record
Abstract
This article argues for a reframing of the curriculum within the academy in order to make the academy more inclusive and more accessible to a diverse student body. Reframing the curriculum is seen as an aspect of decolonizing the university. Many questions emerge from this argument to include the following: What curriculum informs the education contemporary learners receive and how do they apply this to their academic and work lives? How do educators re-fashion their work as educators and also as learners to create more relevant understandings of what it means to be human and to determine what is human work? What are the limits and possibilities of visions of and counter and anti-visions to contemporary education? How do educators and learners challenge colonizing and imperializing relations within the academy and that influence the academy and its learners? How does curriculum become inclusive through teaching, research and graduate training and how does it make space for Indigeneity and multi-centric ways of knowing? How do we frame an inclusive, anti-racist, and anti-colonial global future and what is the work that is required to collectively arrive at that future? These complex questions, stimulated by my decolonizing curriculum work and experience, are engaged through the body of this article.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.077 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".