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Record W4376104485 · doi:10.1163/9789004544031_010

Appendix A

2023· book-chapter· en· W4376104485 on OpenAlexaboutno aff
Julia Landmann

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsAppendixComputer scienceGeologyPaleontology

Abstract

fetched live from OpenAlex

The following is a chronological overview of all the nineteenth, twentieth, and twenty-first century French, German, Spanish and Yiddish borrowings which represent fairly common terms in present-day English.1 The various lexical items are presented according to word-classes.The list also includes borrowings from regional or national varieties (e.g.Canadian French, regional Austrian German, Mexican Spanish, etc), borrowings which are originally and mainly used in AmE, words of Yiddish origin originally or mostly confined to Jewish usage, as well as foreign-language items which are chiefly documented in colloquial English or slang.In the present appendix, the following symbols (placed after a borrowings's earliest recorded usage) are used to identify the different types: * borrowings from national or regional varieties ∞ borrowings originally or chiefly used in AmE ■ borrowings initially or mostly confined to Jewish usage ◇ borrowings which mainly occur in colloquial usage or slang The Chronological Distribution of French Borrowings Since 1801The following list provides an overview of the chronological distribution of French borrowings which were adopted into English since 1801.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.122
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.8780.685

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 head0.059
GPT teacher head0.325
Teacher spread0.266 · 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.

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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Same topicLinguistic Variation and MorphologyFrench-language works237,207