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Record W3116595090 · doi:10.1215/00031283-8791772

North Versus South

2020· article· en· W3116595090 on OpenAlexaff
Stephen Levey, Gabriel DeRooy

Bibliographic record

VenueAmerican Speech · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSpanish Civil WarVariation (astronomy)American EnglishLeverage (statistics)LinguisticsSouth carolinaHistoryContextualizationConstraint (computer-aided design)Relation (database)SociologyPolitical scienceStatisticsArchaeologyMathematicsComputer sciencePhilosophyInterpretation (philosophy)

Abstract

fetched live from OpenAlex

In this article, the authors reconstruct the inherent variability found in mid-nineteenth-century American English by drawing on a corpus of semiliterate correspondence rich in nonstandard grammatical features, the Corpus of American Civil War Letters (CACWL). The primary focus is on a comparison of morphosyntactic variability (was/were variation and restrictive relativization strategies) in letters written between 1861 and 1865 by Civil War soldiers originating from Massachusetts and Alabama. Key findings include the elevated rate of was-leveling, particularly in the Alabama letters; the variable effect of the type-of-subject constraint on the selection of nonstandard was; and the scarcity of wh-relativizers in restrictive relative clauses. Contextualization of these findings in relation to an ongoing quantitative investigation of grammatical variation in four additional states represented in the CACWL (Pennsylvania, Ohio, North Carolina, and South Carolina) provides further evidence of structured heterogeneity in Civil War correspondence as well as the sensitivity of variable grammatical processes to regional differences. Taken together, the study’s findings demonstrate how judicious use of the CACWL can leverage new insights into nineteenth-century American English.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.323
Teacher spread0.273 · 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
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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