Unsettling the Future by Uncovering the Past: Decolonizing Academic Libraries and Librarianship
Bibliographic record
Abstract
Canada is at an interesting point in its history, where the atrocious assimilation practices that were in place until the mid-1990s are being acknowledged in the hopes for a better relationship between Canada’s Indigenous and non-Indigenous peoples. Both the Truth and Reconciliation Commission report, and the Canadian Federation of Library Associations/Federation Canadienne des Associations de Bibliotheques (CFLA/FCAB)’s report from its Truth and Reconciliation Committee (n.d.) recognize the significant role of education needed to address changes. Where do academic libraries fit into this? I first discuss the colonial history of libraries, as extensions of education institutions, followed by a look at how library and information science (LIS) curriculum falls short in preparing students for working with Indigenous peoples and Indigenous resources. Finally, I examine how libraries can decolonize their services. Canadian academic library staff cannot continue to be so ill-equipped to serve Indigenous students and faculty.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.065 | 0.115 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.012 |
| 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".