Indigenous Language Guidelines and Resources
Bibliographic record
Abstract
These collaboratively written guidelines offer suggestions and resources for writing about Indigenous Peoples, knowledge, and histories ethically and respectfully. With an awareness that this work is ongoing, that terms and language are subject to change, and that no set of guidelines or essays can fully account for diverse Native histories, these guidelines and resources offer suggestions for best practices for eighteenth-century scholars who are considering engaging in this work. The authors and contributors of this document understand that language is always evolving and that these guidelines may not be relevant indefinitely and in all circumstances. At some point, we imagine that new guidelines will need to be created. Our goal with the current guidelines is to support colleagues in eighteenth-century studies in writing about Indigenous Peoples ethically and respectfully.
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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 | 0.049 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.131 | 0.050 |
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".