MétaCan
Menu
Back to cohort
Record W6992896190

Metonymy and lexical aspect in English and French

2003· article· en· W6992896190 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Zagreb University Computing Centre (SRCE) · 2003
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsMetonymyVerbNarrativeSentenceEvent (particle physics)InterrogativeAnimacy
DOInot available

Abstract

fetched live from OpenAlex

Metonymy and lexical aspect in English and FrenchIn this paper we provide evidence that conceptual metonymies are cross-linguistically significant in the coding of verbal aspect.Guillemin-Flescher (1981: Ch. 2), in an important contrastive study of narrative texts, notices that English and French differ quite often as to which phase of an aspectual situation is coded in an utterance.To illustrate, compare sentence (1), taken from François Mauriac's well-known novel Thérèse Desqueyroux, with its English translation in (2):(1) Le train ralentit, siffle longuement, repart.(2) The train came to a halt, uttered a long whistle, and started to move again.In the French original (1) the process of moving again is coded.In contrast, it is quite striking that the English translator of (1) prefers to verbalize only the incipient phase of this process by means of an 'inceptive verb construction'-thereby metonymically evoking the process as a whole.We explore the hypothesis that in English, in contrast to French, there is a fairly systematic exploitation of the high-level metonymy SUB-EVENT FOR WHOLE EVENT with the two sub-metonymies INCIPIENT PHASE OF EVENT FOR WHOLE EVENT and ONSET OF EVENT FOR WHOLE EVENT.A corpus search of two different text genres, bilingual transcripts of Canadian parliamentary debates and narrative fiction, reveals that in about 20% of the cases where English has a metonymically interpreted inceptive verb construction, French expresses the equivalent idea directly by means of a single verb form.We relate the findings for the incipient verb construction to the observation that English makes more extended use of the POTENTIALITY FOR ACTUALITY metonymy with perception and mental processing verbs.

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)0.004
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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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

Citations25
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueUniversity of Zagreb University Computing Centre (SRCE)Same topicLanguage, Metaphor, and CognitionFrench-language works237,207