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Record W2474819116 · doi:10.1515/rela-2015-0029

The position of negative adjectives in Aelfric’s Catholic Homilies I .

2015· article· en· W2474819116 on OpenAlexaboutno aff
Maciej Grabski

Bibliographic record

VenueResearch in Language · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and language evolution
Canadian institutionsnot available
Fundersnot available
KeywordsAdjectiveLinguisticsSyntaxNoun phraseNounPrefixPsychologyPhrasePhilosophy

Abstract

fetched live from OpenAlex

In Old English, negative adjectives, i.e. incorporating the negative prefix -un, are said to generally come in postposition to nouns (e.g. Fischer, 2001; Sampson, 2010). This paper investigates to what extent this general rule is followed in Aelfric’s Catholic Homilies, the texts of this author being a typical choice for the study of Old English syntax (cf. Davis 2006; Reszkiewcz, 1966; Kohonen, 1978). The data have been obtained from the York-Toronto-Helsinki Parsed Corpus of Old English Prose (YCOE). The following research questions have been formulated: Do strong negative adjectives outnumber nonnegated adjectives in postposition? Do strong negative adjectives have a tendency to appear in postposition? Do strong negated adjectives occur in preposition? The results indicated that for the sample analyzed, strong adjectives in postposition are not predominantly negated. Additionally, the postposition of most of those which are may potentially be explained by other factors, such as modification by a prepositional phrase, co-occurrence with a weak preposed adjective (both mentioned by Fischer), or indirect Latin influence in a formulaic phrase. Also, the data does not appear to support the observation that negated adjectives tend to appear in post- rather than preposition.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.376
Teacher spread0.269 · 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
Published2015
Admission routes1
Has abstractyes

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