Bibliographic record
Abstract
If one of the most prominent features of early America was the collision of peoples from two hemispheres, much of this collision found expression through different expectations of how men and women should behave. Because participants in these encounters saw that what they thought of as natural attributes of men and women were not consistent across the lines of culture, and because printed descriptions of those encounters expanded the audience for those encounters, it is not an exaggeration to say that early America as a site of contact did much to provoke widespread contemplation of what in the twentieth century came to be known as the distinction between sex and gender. Texts such as John Marrant’s account of his captivity in a Cherokee town and Amerigo Vespucci’s letters illustrate how understanding early America in all its complexity requires accounting for the intricacies of gender as they were performed in intersection with other identity categories such as race. The history of colonialism and the Atlantic slave trade also shows how gender became an instrument of domination. Finally, the stories of figures like Catherine Tekakwitha demonstrate that occasionally individuals found new lives in early America in part by adopting foreign performances of gender.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".