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Record W2810714164 · doi:10.2478/rela-2018-0006

Degree of Grammaticalisation of Behind, Beneath, Between and Betwixt in Middle English

2018· article· en· W2810714164 on OpenAlexfundno aff
Ewa Ciszek

Bibliographic record

VenueResearch in Language · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsnot available
FundersDepartment of Environment and Conservation, Government of Newfoundland and LabradorUniversity of TorontoCollege of Pharmacy, University of MichiganMinisterio de Economía y CompetitividadUniversity of EdinburghUniversität InnsbruckUniversity of MichiganHelsingin YliopistoUniversity of OxfordU.S. Department of Energy
KeywordsGrammaticalizationMiddle EnglishPrefixHistoryPeriod (music)Context (archaeology)LinguisticsOld EnglishDegree (music)LiteratureArtPhilosophyClassicsPhysics

Abstract

fetched live from OpenAlex

The present paper traces the history of four selected adverbs with the prefix be- in Middle English. Already in Old English behind, beneath, between and betwixt are attested to function as both adverbs and prepositions, which demonstrates that the process of grammaticalisation accounting for the development of prepositions from adverbs started before that period. The focus of the study are the diachronic changes of the degree of grammaticalisation of the examined lexemes in the Middle English period as demonstrated by the ratio of their use with a respective function in the most natural context. Hence, specially selected Middle English prose texts are analysed. The analysis shows that while behind and beneath are still frequently used as adverbs in the whole Middle English period, between and betwixt are predominantly used as prepositions already in Early Middle English. This clearly demonstrates that the degree of grammaticalisation of the latter two Middle English words was much higher than that of behind and beneath.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.189
GPT teacher head0.359
Teacher spread0.170 · 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.

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
Published2018
Admission routes1
Has abstractyes

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