International Journal Of Literature And Languages

Optimizing English Pronunciation Instruction for Hanzhonghua Speakers: A Transfer-Based Model for Reducing Cross-Linguistic Phonological Interference

2026-09-03T07:17:17+00:00

English pronunciation difficulties among speakers of Hanzhonghua can be understood partly through the interaction between first-language phonological patterns and the target English sound system. This research develops a transfer-based instructional model designed to identify, prioritize, and reduce cross-linguistic phonological interference affecting English pronunciation among Hanzhonghua-speaking learners. The study adopts a qualitative, theory-driven research and review design based exclusively on the provided literature. Its theoretical foundation integrates language-transfer theory, pronunciation pedagogy, intelligibility and comprehensibility perspectives, second-language research methodology, learner autonomy, and approaches to intonation and discourse. The proposed model consists of five interconnected stages: phonological contrast identification, interference diagnosis, targeted perceptual training, controlled production, and communicative transfer with learner self-monitoring. Particular attention is given to the distinction between pronunciation accuracy and communicative effectiveness, since pronunciation instruction should ultimately support intelligibility and comprehensibility rather than merely approximate native-speaker norms. The analysis indicates that a Hanzhonghua-focused instructional framework should move beyond generalized pronunciation correction and instead establish explicit links between likely first-language influence, perceptual discrimination, articulatory practice, prosodic organization, and communicative performance. The model provides a structured pedagogical architecture that can be adapted to English-major classrooms in Shaanxi Province while recognizing the limitations of generalizing from dialect-level tendencies to individual learners. The study contributes a systematic framework for integrating transfer analysis with contemporary pronunciation instruction and learner-centered practice.

Language, Society, And Identity: Exploring The Role Of Language In Social Interaction

2026-09-04T10:08:49+00:00

Language is never merely a neutral instrument for transmitting information. Every act of speaking simultaneously conveys propositional content and positions the speaker within a web of social relations, signalling regional origin, class, ethnicity, gender, generation, and stance toward the interlocutor. This article synthesises the interdisciplinary scholarship on how language constitutes, reproduces, and occasionally transforms social life. It traces the field’s theoretical foundations linguistic relativity, Bernstein’s codes, Labovian variationism, Goffman’s interaction order, Bourdieu’s linguistic marketplace, and the constructionist turn then examines how language indexes and enacts the principal dimensions of social identity: class, ethnicity and race, gender and sexuality, and age. It turns next to the micro-mechanics of interaction, to language ideology and symbolic power, to multilingual practices including diglossia, code-switching and translanguaging, and to identity work in digital environments. Drawing on current figures, it documents the consequences of global linguistic inequality: of roughly 7,100–7,200 living languages, some 42–43 per cent are endangered, while English alone accounts for nearly half of all web content. It closes with a review of methods, applied implications, and directions for research in an era of algorithmic mediation.

Using Large Language Models for Automated Corpus Annotation and Linguistic Analysis: A Critical Methodological Framework

2026-09-04T10:19:35+00:00

Large language models (LLMs) are increasingly used to classify, label, summarize, and interpret large text collections, creating new possibilities for corpus linguistics. Their capacity for zero-shot and few-shot instruction following could reduce the cost of linguistic annotation and extend analysis beyond the categories handled by conventional part-of-speech taggers, parsers, and dictionary-based tools. At the same time, LLM outputs are probabilistic, prompt-sensitive, model-dependent, and potentially biased, raising fundamental questions about measurement validity, annotation reliability, and reproducibility. This article critically synthesizes foundational corpus-annotation principles with recent evidence on LLM-based text annotation and develops a validated human–LLM workflow for corpus research. The framework distinguishes token-, span-, sentence-, document-, and discourse-level annotation; requires a human-coded gold sample before large-scale deployment; treats prompt design as part of the annotation manual; and evaluates accuracy, precision, recall, F1, inter-annotator agreement, stability across repeated runs, subgroup performance, and error types. Recent studies show that LLMs can approach or exceed crowd-worker performance on some well-specified classification tasks, but that performance varies substantially across datasets, languages, models, prompts, text lengths, and annotation complexity. Span-level annotation and context-dependent semantic or pragmatic coding remain particularly challenging. The article therefore argues against unvalidated full automation and proposes selective automation, disagreement-based human adjudication, model/version documentation, and preservation of raw outputs. For corpus linguistics, the strongest near-term use of LLMs is as flexible annotators within a transparent, theory-driven, and auditable pipeline rather than as replacements for linguistic expertise. The resulting framework supports scalable corpus annotation while preserving the empirical principles on which corpus-based linguistic inference depends.