Benjamin Lee Whorf and Ernest Naquayouma’s Working Relationship: A Perspective on Linguistic Fieldwork in the 1930s
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.049 | 0.036 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".