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Record W2320830710 · doi:10.1017/s1360674315000465

Old ‘truths’, new corpora: revisiting the word order of conjunct clauses in Old English

2016· article· en· W2320830710 on OpenAlexaboutno aff
Kristin Bech

Bibliographic record

VenueEnglish Language and Linguistics · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsnot available
Fundersnot available
KeywordsVerbLinguisticsWord orderPoint (geometry)Order (exchange)Nominative caseComputer sciencePhilosophyMathematics

Abstract

fetched live from OpenAlex

In Bech (2001a, 2001b), I took issue with the oft-repeated claim that Old English conjunct main clauses are commonly verb-final, and disproved it. However, the myth persists. In the meantime, theYork–Toronto–Helsinki Parsed Corpus of Old English Prose(YCOE, Tayloret al.2003) has been created, so the time has come to revisit this topic and consider it in light of new, extensive and generally accessible data. Using the YCOE corpus, I confirm and expand on Bech's (2001a, 2001b) empirical findings, showing that (i) OE conjunct clauses are neither typically verb-final nor verb-late, but they are more frequently verb-final and verb-late than non-conjunct clauses are; and (ii) verb-final and verb-late clauses are typically conjunct clauses. These two perspectives must be kept apart: in the first, the starting point is the entire body of conjunct clauses, and in the second it is the entire body of verb-final/verb-late clauses. I propose that the failure to distinguish between the two perspectives, i.e. whether it is conjunct clauses or word order that constitutes the point of departure, is the origin of the misconception concerning conjunct clauses and word order. In order to establish whether this distinction has been fuzzy all along, or whether it must be ascribed to distorted referencing in the course of a century of research, I trace the research on this topic back to the end of the nineteenth century. I show that the alleged verb-finality of conjunct clauses may be ascribed to awhisper-down-the-laneeffect – the retelling of the story has changed the story.

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)0.007
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.044
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.007
Science and technology studies0.0030.007
Scholarly communication0.0050.014
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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 designObservational
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

Citations12
Published2016
Admission routes1
Has abstractyes

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