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Record W3177708785 · doi:10.1080/13645145.2021.1949094

“You feel embodied with the cataract:” American girls, landscape and national identity in the early republic

2020· article· en· W3177708785 on OpenAlexaboutno aff
Sharon Halevi

Bibliographic record

VenueStudies in Travel Writing · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsnot available
FundersIsrael Science Foundation
KeywordsEmbodied cognitionIdentity (music)Gender studiesGeographyNational identityHistoryAestheticsSociologyPolitical scienceArtLawEpistemologyPoliticsPhilosophy

Abstract

fetched live from OpenAlex

This article examines how American girls and young women in the early republic formed a new sense of a “corporeal” national identity while touring areas of the Hudson River valley and the Great Lakes. Based on a reading of the travel writings of fifteen white, middle and upper-middle class, American girls and young women travelling between 1802 and 1835, it demonstrates first that during the tours within the United States the girls underwent a multisensory familiarisation with the landscape, which both bolstered their confidence and concretised much of their theoretical knowledge gained during their studies. Second, when touring the Canadian shores of the Great Lakes their focus was on constructing both its landscape and its people as “other”. The article closes with a consideration of how these young women’s travel writings may offer a new perspective for the study of a gendered national identity formation in the early United States.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.124
GPT teacher head0.324
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations1
Published2020
Admission routes1
Has abstractyes

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