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Women and Science

2013· reference-entry· en· W2791780502 on OpenAlexvenueno aff
Alix Cooper

Bibliographic record

VenueRenaissance and Reformation · 2013
Typereference-entry
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsnot available
Fundersnot available
KeywordsCopernicusPeriod (music)MultitudeScientific revolutionNatural (archaeology)GeniusWomen in scienceNatural scienceEarly modern periodHistory of scienceHistoryNature of ScienceNarrativeNatural philosophySocial scienceSociologyScience educationLiteratureEpistemologyArt historyAestheticsArtPhilosophyGender studiesAncient historyArchaeologyPedagogyAstronomyPhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.412
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.222
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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