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Record W7071537452

Syntactic, Semantic, and Stylistic Variation in Old English Periphrastic Passives

2024· article· en· W7071537452 on OpenAlexaboutno aff

Bibliographic record

VenueScholar Commons (University of South Carolina) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsParticipleResultativeOld EnglishSubject (documents)ScholarshipAgency (philosophy)Point (geometry)Embarrassment
DOInot available

Abstract

fetched live from OpenAlex

This dissertation establishes a new method for better understanding the Old English (OE) periphrastic passive (PerPass) system by bridging the gap between traditional qualitative methods and contemporary corpus-based methods in Historical Linguistic research. In doing so, this research resists the tendency in modern scholarship to extract agency and personhood from OE speakers, and instead approaches them as agentive and creative participants in their language and its grammar. The OE PerPass is understood to be constructed with a BE-verb (beon and wesan ‘be, was’ or weorðan ‘become’) and a coordinated past participle (ex. 1-2). 1. And wearð sona gehæled þurh þæs halgan mihte. ‘And [he] was immediately healed through the saint’s power.’ Ælfric's Lives of Saints [coaelive,+ALS_[Martin]:943.6577] 2. …unarimede manna untrumnessa ðær wæron oft & gelome gehælde… ‘There innumerable illnesses of men were often and frequently healed.’ Blickling Homilies [coblick,LS_25_[MichaelMor[BlHom_17]]:209.218.2663] Though this formula appears simple, and is remarkably like Modern English’s passive, the OE PerPass has been the subject of much scholarly debate. On one hand, traditional approaches have not been able to account for the full range of meanings communicated by this construction (cf. Abraham, 1992; Jones, 2009; Muller, 2009). On the other, contemporary approaches have problematized the concept of a ‘real’ OE passive altogether, claiming that it is a resultative adjectival construction with no verbal status. Proponents of this theory point to the alleged inconsistencies in the PerPass formula to support this claim, such as the inconsistent presence of participial inflections, seemingly overlapping or contradictory meanings of the BE-verb auxiliaries, and the subsequent loss of weorðan in the Middle English period (Petré & Cuyckens, 2008, 2009; Petré, 2010, 2013, 2014; Mailhammer & Smirnova, 2013; Martín Arista & López, 2018). However, such perspectives are often divorced from the textual and generic contexts of each token, as well as the broader Germanic context of OE itself. Thus, discrepancies in the data are treated as incoherencies within OE, rather than evidence of the propensity for individuals within the language to behave creatively. Using the York-Toronto-Helsinki Parsed Corpus of Old English (Taylor et al., 2003) and the York-Helsinki Parsed Corpus of Old English Poetry (Pintzuk & Plus, 2001), this dissertation combines broad-scale quantitative corpus-based methods with smaller-scale qualitative approaches to the data, abandoning efforts to date a precise moment of passive grammaticalization, and instead exploring the breadth of literal and implied meanings to be found within the OE PerPass formula. As such, this dissertation presents new conclusions about the OE PerPass: first, that there was a clearly established, complex passive system across the whole of the OE period; second, that the apparent ‘inconsistencies’ often noted in the passive formula are features of OE at large, rather than contradictions in the passive formula itself; and, third, that approaches to this topic that consider only broad-scale corpus-based data fail to recognize agency and individuality of OE authors and their ability to interact creatively with the grammatical structures of their language. Finally, this dissertation contributes to a new generation of Historical Linguistic study that combines both qualitative and quantitative methods to identify interesting quandaries in the historical data without excising that data from its proper textual and historical contexts.

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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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.243
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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