Benjamin Lee Whorf and Ernest Naquayouma’s Working Relationship: A Perspective on Linguistic Fieldwork in the 1930s
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 1932, Benjamin Lee Whorf, a fire insurance analyst, began studying the Hopi language with Ernest Naquayouma, a Hopi tribal member. This article asks how Whorf and Naquayouma’s working relationship came to be and what they were talking about across their seven years of meetings together. First, I situate their relationship within the broader historical context of the 1930s, extending Regna Darnell’s concept of “invisible genealogies” beyond the academy, highlighting how linguistic consultation was but one among many ways Hopi language and culture were being presented to non-Hopi audiences. Secondly, drawing on archival sources, I show how Naquayouma participated in working sessions as someone with proficiency in Hopi, but also as an individual accountable to a set of values that exceeded the research encounter. The holistic view of language that Whorf arrived at after 1937 arose at least in part from Naquayouma’s fullness of presence as an interlocutor.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it