Bibliographic record
Abstract
Applying a science studies approach to early American literature means focusing on how early modern settler colonialism in the Americas, with all its violence and exploitation, was a knowledge-producing machine. Enslavers and colonizers stole the skills, labor, and resources from enslaved Africans and Indigenous peoples, and in the process forged many of the empirical practices, forms of measurement and categorization, and stratification between types of expertise that we typically recognize as constituting scientific work. Research in early American literature investigates the complexity of particular representations of natural phenomena and traces their circulation within or against powerful narratives that organized culture. This shows how contemporary scientific understandings of natural phenomena are historically and culturally determined and calls attention to the settler colonial work scientific expertise can continue to do in the present and contributing to the project of imagining alternative uses for it. This chapter argues for an approach to reading nature in early American literature that is modeled on acts of translation rather than processes of decoding. This difference is as subtle as it is essential for opening up the present to simultaneous scrutiny as critics confront an archive produced by the violent structures of the past.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".