Bibliographic record
Abstract
Until relatively recently, women were seen as having played little part in the “Scientific Revolution” of the 16th and 17th centuries. Textbook narratives of the transformations in astronomy and physics inaugurated by Nicolaus Copernicus (b. 1473–d. 1543) and brought to completion by Isaac Newton (b. 1642–d. 1727) told a heroic story of the intellectual achievements of exceptional men of genius. Over the past several decades, however, research investigating the actual practice of science during this period—or, to be more accurate, the wide range of activities we nowadays see as comprising natural science—has revealed a multitude of ways in which women were, in fact, involved in the production of natural knowledge. As historians of science have shown, women of the early modern period carried out astronomical observations, conducted experimental procedures in distillation, theorized about the nature of nature, and even traveled vast distances in order to study the flora and fauna of far-off places. Scholars examining early modern social and cultural patterns that excluded women, and there were many, have also discovered numerous factors that enabled women of this period to participate in natural inquiry. Studies of the role of ideas of gender more broadly in early modern ideas of “nature” have further enriched understanding of women and science.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".