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Record W2488817173 · doi:10.1075/ahs.4.02per

Political perspectives on linguistic innovation in independent America

2015· book-chapter· en· W2488817173 on OpenAlexaff
Carol Percy

Bibliographic record

VenueAdvances in historical sociolinguistics · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeologismPoliticsLinguisticsLexisSpellingSubject (documents)EnthusiasmFederalistFocus (optics)Political scienceHistoryComputer scienceLibrary sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

In this study, I focus the large subject of Late Modern English in newly independent America through the lens of the politician and scholar Thomas Jefferson. Drawing on evidence relating to the several libraries he assembled serially over his lifetime, especially on catalogues and correspondence, I focus particularly on lexis and on lexicography – especially, how American neologisms and a new American dictionary might relate both to linguistic tradition in Britain and to political affiliations in America. The grammars and dictionaries catalogued in Jefferson’s library hint at this politician’s lifelong interest in English usage. The correspondence connected with his library (including the spelling of his letters) demonstrates in more detail that Jefferson was interested in American neologisms and in non-standard spelling. In brief, Jefferson’s republican, anti-federalist political principles are consistent with his linguistic opinions, especially with his resistance to imposed reform despite his enthusiasm for lexical and orthographical innovation. In turn, these epistolary debates remind us of the linguistic consequences of political divisions within the new republic.

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)0.001
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.094
GPT teacher head0.454
Teacher spread0.360 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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