Comparing the Cataloguing of Indigenous Scholarships: First Steps and Finding
Bibliographic record
Abstract
This paper provides an analysis of data collected on the continued prevalence of outdated, marginalizing terms in contemporary cataloguing practices, stemming from the Library of Congress Subject Heading term “Indians” and all its related terms. Using Manitoba Archival Information Network’s (MAIN) list of current LCSH and recommended alternatives as a foundation, we built a dataset from titles published in the last five years. We show a wide distribution of LCSH used to catalogue fiction and non-fiction, with outdated but recognized terms like “Indians of North America-History” appearing the most frequently and ambiguous and offensive terms like “Indian gays” appearing throughout the dataset. We discuss two primary problems with the continued use of current LCSH terms: their ambiguity limits the effectiveness of an institution’s catalog, and they do not reflect the way Indigenous Peoples, Nations, and communities in North America prefer to represent themselves as individuals and collectives. These findings support those of parallel scholarship on knowledge organization practices for works on Indigenous topics and provide a foundation for further work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.031 | 0.052 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".