An Analysis of Figurative Language in Laurie Halse Anderson’ Wintergirls
Bibliographic record
Abstract
This study examines the use of figurative language in Laurie Halse Anderson’s 2009 novel Wintergirls, focusing on both its stylistic variety and semantic function. Specifically, the study (1) identifies and classifies types of figurative language employed, and (2) analyzes the meanings conveyed using Leech’s (1981) seven types of meaning. The systematic random sampling method involved the selection of 13 chapters from which 224 excerpts were extracted for analysis. Coding guidelines, tables, and excerpts comprise the research instruments. Each form of figurative language and type of meaning was categorized to determine their frequencies and then presented as percentages. The study adopted the validation methodology of Miles and Huberman (1994) to ensure reliability. Findings reveal that metaphor is the most frequently used figurative device (f = 89, 39.73%), while connotative meaning emerges as the most dominant semantic category (f = 51, 22.77%). The results suggest that figurative language in Wintergirls not only enhances aesthetic quality but also serves as a vehicle for expressing emotional trauma, identity struggle, and psychological complexity. These insights contribute to literary stylistics and offer pedagogical value for English as a Foreign Language (EFL) instruction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".