Effective Indigenous Terminology in Canadian Legal Research for the Arctic
Bibliographic record
Abstract
Terms used in today’s society to describe Indigenous Peoples and cultures are significantly different than historical terminology. Contemporary Arctic and Indigenous researchers will know current keywords to conduct their research, but may not be able to locate historical documents if they are not cognizant of the changing terms used throughout history. This paper will analyze appropriate contemporary and historical keywords in the context of Canadian legal research best practices. Keywords used to effectively find Aboriginal resources will illustrate changes in taxonomy reflecting changes in societal norms, database practices, legal definitions, and the various jurisdictions of Aboriginal Peoples. A survey of Canadian law libraries will be conducted to analyze subject headings found in library catalogues, legal indexes, and other primary and secondary resources. Given the interdisciplinary nature of law, this paper will be applicable to most Indigenous scholars across the Social Sciences and Humanities.
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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.042 | 0.036 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".