Decolonizing Librarians’ Teaching Practice: In Search of a Process and a Pathway
Bibliographic record
Abstract
Many educators across post-secondary institutions are learning about their colonial histories and the need to decolonize curriculum, learning materials, and teaching practice described in the Truth and Reconciliation Commission of Canada: Calls to Action (2015). This qualitative study explored the meaning of decolonizing with a group of ten instruction librarians at a mid-sized Canadian institution. The project was conducted in the form of a learning program to offer the predominantly white settler librarian participants a chance to explore these topics. It provided an opportunity to document a learning process and a pathway to initiate change. A five-month learning program uncovered participant questions and interpretations of decolonizing drawing on transcripts of individual learning journals and a focus group as the data set. The program inspired a community of practice enabling the learning and unlearning essential to decolonizing. We report, from the perspective of two white settler librarians, on the meaning of decolonizing as an ongoing process that enables awareness of colonization, personal identity, and positionality and includes strategies librarians can use on the path to decolonizing teaching, collections, and spaces. Participant self-awareness surfaced a critical librarianship mindset where information is understood as a product shaped by cultural, historical, social, and political forces, and where we acknowledge that academic libraries and their information sources and systems are not neutral and empower specific voices.
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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 | 0.029 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.046 | 0.093 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".