MétaCan
Menu
Back to cohort
Record W2596636279

Representations of French linguistic borrowing in early modern England

2016· dissertation· en· W2596636279 on OpenAlexfundno aff
Silje Normand

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2016
Typedissertation
Languageen
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsnot available
FundersCollege of Pharmacy, University of MichiganUniversity of TorontoUniversity of Michigan
KeywordsLinguisticsHistoryPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines the early modern debates surrounding the incorporation of French loan words into the English vocabulary through an analysis of the prefaces of a variety of early modern English dictionaries and a selection of Restoration plays, political pamphlets and tracts.It considers the long seventeenth century, starting with Robert Cawdrey's dictionary of 'hard words', A table alphabeticall (1604) and ending with Samuel Johnson's A Dictionary of the English Language (1755).The thesis is not concerned with whether particular words are in fact French loans, nor with the chronology of their usage, but rather examines the debates surrounding linguistic borrowing identified as French by contemporaries.Neither is the emphasis on the actual words themselves, as much as on the attitudes towards their use and the portrayal of those that use them.The thesis analyses the representation of French linguistic borrowing in three domains of discourse (dictionaries, Restoration satire, political pamphlets and periodical essays), paying particular attention to the metaphors and images that are employed in these representations.Taking into consideration ideas of linguistic purism and language corruption, it explores how representations of French borrowing can be situated within a larger historical context of English nation building and fluctuating Anglo-French relations.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueBIBSYS Brage (BIBSYS (Norway))Same topicHistorical Linguistics and Language StudiesFrench-language works237,207