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Record W4388961712 · doi:10.1093/applin/amad067

Playing with second language metaphor: An exploration with advanced Chinese learners of English

2023· article· en· W4388961712 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Linguistics · 2023
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsKensington Health
Fundersnot available
KeywordsLinguisticsPsychologyComprehensionMandarin ChineseMetaphorCompetence (human resources)Communicative competenceConceptual metaphorPedagogy

Abstract

fetched live from OpenAlex

Abstract The present study continues research that takes non-serious language more seriously (Cekaite and Aronsson 2005) by focusing on a central second language (L2) Metaphoric Competence factor, Metaphor Language Play (MLP). For willing learners, MLP offers a diversity of benefits (Bushnell 2009; Bell 2012a) despite being one of the most challenging Metaphoric Competence aspects (O’Reilly and Marsden 2021). While studies provide rich descriptions of naturally occurring MLP, elicitation approaches are needed to target comprehension/production of specific forms/meanings/usages and types of play, for example, comprehension of US sitcom humour (Dore 2015). With 69 advanced first-language Mandarin L2 English learners, we addressed these issues via an Exploratory Factor Analysis to uncover hitherto unknown relationships between written/spoken/receptive/productive MLP measures, and a thematic analysis of the linguistic, conceptual, and metalinguistic themes in learners’ MLP. The findings revealed three underlying MLP factors, two positively related, and a rich set of linguistic, conceptual, and metalinguistic themes. The implications of findings for future research and pedagogy are discussed.

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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.194
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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