Playing with second language metaphor: An exploration with advanced Chinese learners of English
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".