Indigenization of Knowledge Organization at the Xwi7xwa Library
Bibliographic record
Abstract
This paper examines the Indigenization of knowledge organization within library and information studies through conceptual analysis and a descriptive case study of an Aboriginal academic library, the Xwi7xwa Library at the University of British Columbia, Canada. We begin by locating the library in place and time, review its historical development in the context of Indigenous education in Canada and describe the evolution of its unique Indigenous classification scheme and related Indigenous subject headings. This place-based analysis leads to a particular articulation of Indigenization and a conceptual framework for Indigenization of knowledge organization at the Xwi7xwa Library, which guide the practice of knowledge organization design and modes of mobilization at this particular Aboriginal library. The conceptual framework rests on two basic assumptions: firstly, that collection development is curatorial in nature and is the seminal step in library knowledge organization, and, secondly, that the Indigenized knowledge organization system is critical to effective Indigenous information and instructional services, programming and research at the Library. The final section presents future possibilities for the Indigenization of knowledge organization through convergences and collaborations with emerging networks of Indigenous scholars and Indigenous communities of knowledge within the context of new technologies.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.034 | 0.053 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".