MétaCan
Menu
Back to cohort
Record W2476526270 · doi:10.1017/cbo9780511485497.002

When travelers swarm forth: antebellum urban aesthetics and the contours of the political

2003· book-chapter· en· W2476526270 on OpenAlexaff
Mary Esteve

Bibliographic record

VenueCambridge University Press eBooks · 2003
Typebook-chapter
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsConcordia University
Fundersnot available
KeywordsCrowdsPolityPoliticsDemocracyCharismaGentrificationSuffragePolitical economyHistoryPolitical scienceLawEconomic historySociologyEngineering

Abstract

fetched live from OpenAlex

When Walt Whitman, democratic crowd champion bar none, salutes the people of the polity, he looks to the masses crossing Brooklyn Ferry, the crowds milling about Manhattan's commercial district, the tides flowing through Broadway. In other words, he does not look to explicitly political crowds, such as those in Baltimore rioting against rampant bank faults in the late 1830s, or those in upstate New York rebelling against rents on long term leases in the 1830s and 1840s, or even those widely admired Dorrites demanding suffrage expansion and forming an extra-legal People's Convention to protest the elected state government in Rhode Island in 1842. Similarly, when Hawthorne scrutinizes what it means to be a "naturalized citizen," he turns to an everyday crowd scene: a train-station peddler selling his goods to the "travellers [who] swarm forth." Such literary enterprises testify to the trend, begun in the antebellum period, to displace revolutionary crowds with urban crowds in representations of the fledgling democracy's populace. They accord with Tocqueville's observation in 1838 that "[a]t this moment perhaps there is no country in the world harboring fewer germs of revolution than America." Indeed such crowd representations bear the mark of a polity preoccupied less with self-installation than self-maintenance.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.957
Threshold uncertainty score1.000

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 studies0.0000.003
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.187
Teacher spread0.162 · 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.

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
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooksSame topicVisual Culture and Art TheoryFrench-language works237,207