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Record W6996572510

The semantics and syntax of Old English verbs of change of state: depriving, increasing, and learning

2021· other· es· W6996572510 on OpenAlexaboutno aff

Bibliographic record

VenueRIUR (Universidad de La Rioja) · 2021
Typeother
Languagees
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSyntaxSemantics (computer science)GrammarMandarin ChineseSimple pastParsing
DOInot available

Abstract

fetched live from OpenAlex

Esta tesis doctoral trata del léxico verbal del inglés antiguo desde una perspectiva sincrónica con el objetivo de estudiar el enlace entre la semántica y la sintaxis de tres clases de verbos del inglés antiguo que implican un cambio de estado: privación, incremento y aprendizaje. El marco teórico en el que se basa este trabajo es el de las clases verbales y la Gramática del Papel y la Referencia. Los datos para el ánalisis han sido extraidos de dos fuentes textuales (Dictionary of Old English Corpus y York-Toronto-Helsinki Parsed Corpus of Old English) y varias fuentes lexicográficas que incluyen diccionarios, diccionarios electrónicos, tesauros y bases de datos léxicas. Las conclusiones principales obtenidas de este estudio tienen que ver con la coherencia de las clases verbales de privación y de incremento en inglés antiguo en lo que se refiere a las alternancias y las construcciones en las que estos verbos participan, y con la flexibilidad de la Gramática del Papel y la Referencia, puesto que permite un análisis teórico de orientación semántica de los verbos de aprendizaje del inglés antiguo.

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: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0030.004
Open science0.0000.001
Research integrity0.0000.001
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 head0.012
GPT teacher head0.225
Teacher spread0.212 · 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
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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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