Shortcomings of Bibliographic Description in Service of Indigenous Peoples in Canada
Bibliographic record
Abstract
The marginalization of Indigenous Peoples in library catalogues and cataloguing standards is well documented. This article looks beyond Library of Congress Classification to analyze how the marginalization of Indigenous Peoples manifests in Machine Readable Cataloguing (MARC) and online public access catalogs (OPACs) to the detriment of Indigenous users. The rules that govern bibliographic description either obscure the presence of materials in a collection that represent Indigenous worldviews, or do not have the capacity to accurately record demographic terms related to Indigenous Peoples. This leads to inaccurate access points and culturally inappropriate metadata. Examples of projects and institutions innovating in this domain are examined. The harms cataloguers enact through adherence to bibliographic standards deserve critical and ethical analysis. These analyses and innovative projects are first steps towards better serving Indigenous users and reconciliation in libraries in Canada.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.030 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".