A Stylistic Analysis in Literary Texts: Fuzzy-set Qualitative Comparative Analysis in Everett's James
Bibliographic record
Abstract
This study investigates the correlation between stylistic features and thematic development in Percival Everett's James (2024) through an innovative integration of traditional stylistic analysis with fuzzy-set Qualitative Comparative Analysis (fsQCA). The research examines four primary stylistic elements metaphors, symbolism, imagery, and sentence structure and their relationship to four thematic categories: struggle, hope versus despair, freedom, and confinement. Using a mixed-method approach, the study combines qualitative textual analysis with quantitative correlation analysis to evaluate the strength of stylistic-thematic relationships. Data analysis reveals strong to very strong correlations between specific stylistic features and themes, with metaphors most strongly correlated with struggle (r = 0.85), symbolism with hope versus despair (r = 0.80), imagery with freedom (r = 0.78), and short sentences with confinement (r = 0.76). The findings demonstrate that linguistic elements function as essential tools for thematic coherence rather than merely decorative devices, providing empirical evidence for systematic relationships between form and meaning in literary texts. By integrating fsQCA methodology with literary analysis, this research offers a replicable framework for future stylistic studies while contributing to methodological innovation in digital humanities and computational literary analysis.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".