Bibliographic record
Abstract
Today W. Duncan Strong (1899-1962) has been all but forgotten in the history of American archaeology.He made no front-page discoveries or theoretical breakthroughs; his single claim to fame was his contribution to Great Plains archaeology.This work, carried out in the early 1930s at the beginning of his career during the otherwise unremarkable Nebraska State Archaeological Survey, merits a place in the discipline's history merely because his were the first substantial publications on this previously neglected region.For students of American literature and culture, however, his Nebraska field journals and publications offer an extraordinary opportunity for investigating popular images of the Great Plains.Specifically, Strong's writings reveal the ways in which stories about the American frontier, from historical documents and scientific reports to novels and folk memories, shaped perceptions of the region's landscape and history.Strong craved adventure, and he found it equally in archaeology and in books.Over the course of his career, he wintered with a band of nomads in Labrador, lost two fingers in a hunting accident in Honduras, and excavated numerous ancient sites in Peru.When real-life excitement slowed, he read adventure stories.It is telling that, while supervising a New Deal work relief dig in California, he read books about exploring Brazil, riding horseback through the Americas, and pioneering in the San Joaquin Valley. 1 He especially loved the
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".