Pedagogical stylistics in multiple foreign language and second language contexts: A synthesis of empirical research
Bibliographic record
Abstract
This article examines the efficacy of pedagogical stylistics as a learning tool for developing second or foreign language proficiency. Pedagogical stylistics – an instrument for investigating the linguistic, sociocultural and dialogic features inherent in literary and non-literary texts – has often been criticized for relying too heavily on intuition rather than empirical support to substantiate its employment in language learning classrooms. To better understand this criticism a coding framework adapted from previous research was employed to synthesize 13 studies across four, second or foreign languages in nine countries. Three themes emerged from this synthesis: (1) stylistics as a tool for improving L2 performance; (2) stylistics’ contribution to building language awareness; (3) stylistics as a tool for building academic skills beyond L2 acquisition. This work explores these themes and discusses the research practices informing the claims made therein, highlighting a consistent underreporting or under collecting of data as a recurring problem in the literature. This shortcoming precludes a meta-analysis of the literature, and this article argues that this shortcoming contributes to a justifiably weak representation of stylistics in second or foreign language contexts. To rectify this issue suggestions are made for more thorough reporting of data and a more robust research agenda in second or foreign language-based, stylistic contexts.
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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 | 0.021 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".