The Respectful Terminologies Platform Project and Envisioning Indigenous Governance
Bibliographic record
Abstract
This paper will discuss the Respectful Terminologies Platform Project (RTPP), a project focused on creating a system of Indigenous terminologies, and questions of governance within cataloging and other descriptive practices. As an emerging Indigenous-lead project created through years of advocacy work, RTPP is engaged in work to vision a means of Indigenous vocabulary development focused on community governance and protocols. At the same time, existing governance systems for terminology and vocabulary systems such as the Library of Congress, and the Canadian Subject Headings, and projects such as the Homosaurus serve as examples of different models of governance. This paper will explore concepts of governance, the role of UNDRIP in systems of terminology, and Principles such as CARE. Woven throughout the paper will be moments to envision a system which human rights as the central guiding consideration for systems of terminology.
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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 | 0.050 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.005 |
| 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".