Barriers and Collaborations in Decolonization and Indigenization of Library and Information Studies (LIS) Programs in Canada
Bibliographic record
Abstract
Decolonization and Indigenization of Library and Information Studies (LIS) curriculum is a crucial undertaking in the Canadian context, especially in light of the work of the Truth and Reconciliation Commission (TRC), the adoption of UNDRIP, and national discussions around reconciliation. While there have been varied initiatives at Canadian LIS schools, structural barriers including accreditation requirements and institutional siloing among others inhibit the development of pan-Canadian collaborations. After a review of the literature, this paper explores the multiple barriers to decolonization and Indigenization of LIS curriculum in a Canadian context and then examines the work National Indigenous Knowledge and Language Alliance (NIKLA), and Indigenous led partnerships, in advancing work in this area. The paper concludes by discussing future work planned by NIKLA and its Indigenous Curriculum working group, while also noting future challenges.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.035 | 0.011 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".