Bibliographic record
Abstract
Information literacy scholars and leaders are calling for the decolonization of library instruction, knowing that our work helps to maintain colonial systems. While there is no checklist or road map to program decolonization, academic libraries and instruction teams must start the work anyway. This article shares the story of curriculum decolonization at Western Libraries, so far, including the decolonization ‘cycle’ we followed and our resulting six learning outcomes. Grounded in epistemic justice, our new curriculum prioritizes living beings over information, and uses a broad, inclusive definition of knowledge throughout. Librarians at Western University acknowledge that the first step in decolonization is making space for multiple ways of knowing and that white librarians have particular responsibilities within this work to decolonize their minds. While our curriculum is far from perfect, we invite other educators to use and adapt our learning outcomes, as well as the decolonization approach and reflection questions shared here.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.047 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".