Interpretresses: Native American Women Translators in Colonial America
Bibliographic record
Abstract
Underlying all the disputes and treaties between native Americans and Europeans was the need for an understanding of what the groups were saying to each other. Translation was the common denominator throughout the numerous interactions between native tribes in America and colonists coming over from Europe. In colonial America, translators were crucial to establishing relationships between native Americans and the Europeans that came to North America to create colonies. These interpreters operated in the in-between of two different cultures and they needed to be knowledgeable enough about both of them to correctly convey meaning to either side. It was also a space that many women occupied. Who were these women? How did they come to be translators and why were they chosen? The answer is different and varied for every native American tribe and every native American person. This paper will look at three different women from three different tribes to analyze the differences and similarities between native American translators and the significance of them. The women looked at in this paper are Thanadelthur, a translator for the Hudson River Company, Mary Musgrove from the Creek nation, and Madame Montour, a Métis woman from Canada. In this paper, I argue that the women who worked as translators were able to harness a unique power and authority within both their cultures despite the limitations their gender placed on them. This power was finite and entirely dependent on how the women were viewed within their respective tribes. The role of interpreter also required a certain amount of privileges, namely education and familial connection.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.020 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".