Research Methods in Applied Linguistics最新文献

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Indonesian cross-linguistic named entity recognition 印尼语跨语言命名实体识别
Research Methods in Applied Linguistics Pub Date : 2025-07-08 DOI: 10.1016/j.rmal.2025.100236
Danang Arbian Sulistyo , Aji Prasetya Wibawa , Didik Dwi Prasetya , Fadhli Almu’iini Ahda
{"title":"Indonesian cross-linguistic named entity recognition","authors":"Danang Arbian Sulistyo ,&nbsp;Aji Prasetya Wibawa ,&nbsp;Didik Dwi Prasetya ,&nbsp;Fadhli Almu’iini Ahda","doi":"10.1016/j.rmal.2025.100236","DOIUrl":"10.1016/j.rmal.2025.100236","url":null,"abstract":"<div><div>This study examines the potential of Named Entity Recognition (NER) in translating cross-biblical texts of Indonesian, Madurese, and Javanese. The goal is to enhance translation precision by incorporating entity categorization. The approach involves training an NER model using Conditional Random Fields (CRF) and evaluating its performance on the Book of Joshua. The annotated dataset includes features such as word identity, shape, part-of-speech identifiers, and semantic information. Tagging the data with labels such as Person, Location, and Organization reveals variations in effectiveness across languages. Indonesian yields the highest F1 score (78.69), reflecting consistent performance across all parameters. Although Madurese achieves a high recall for Location entities (82.16), its precision is lower (74.99). Javanese demonstrates strong precision in identifying locations (77.46), but a slightly lower recall score (77.21). The findings suggest the need to tailor the NER model to suit the specific characteristics of low-resource languages for improved translation quality.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Investigating LID in four test conditions - Do instructions, test formats and item positioning matter? 在四种测试条件下调查LID -说明,测试格式和项目定位重要吗?
Research Methods in Applied Linguistics Pub Date : 2025-07-04 DOI: 10.1016/j.rmal.2025.100233
Hung Tan Ha , Duyen Thi Bich Nguyen , Tim Stoeckel
{"title":"Investigating LID in four test conditions - Do instructions, test formats and item positioning matter?","authors":"Hung Tan Ha ,&nbsp;Duyen Thi Bich Nguyen ,&nbsp;Tim Stoeckel","doi":"10.1016/j.rmal.2025.100233","DOIUrl":"10.1016/j.rmal.2025.100233","url":null,"abstract":"<div><div>Recent research has found the Updated Vocabulary Levels Test (UVLT) to have Local Item Dependence (LID), a violation to the central assumption of all Rasch and Item Response Theory models. LID in the UVLT is hypothesized to be caused by a feature of matching tasks: once an option is selected for one target word, it will not be selected for another. It is also hypothesized that if this feature is removed, LID will be reduced. The present study investigated the effects of LID in four test conditions. The first employed the 3:6 matching format of the UVLT with no instruction concerning option recycling. The second used the same format but with instructions encouraging option recycling. The third utilized a multiple-choice format, with items belonging to the same UVLT cluster using identical sets of 6 options and placed adjacently. The fourth also used a multiple-choice, 6-option format, but items sharing identical options were far apart, making them less “local”. Data from 231 Vietnamese EFL learners were analyzed using Rasch unidimensional modelling and Rasch Testlet Modelling (RTM). Person estimates from the unidimensional models and the general dimensions from the RTMs were compared and correlated. Substantial LID was present in Conditions 1–3. Significant distortions of person estimates were found in all test conditions. However, the findings showed that LID had a negligible impact on person ordering in all test conditions.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Do data collection methods matter for self-reported L2 individual differences questionnaires? In-person vs crowdsourced data 数据收集方法对自我报告的第二语言个体差异问卷有影响吗?面对面vs众包数据
Research Methods in Applied Linguistics Pub Date : 2025-07-03 DOI: 10.1016/j.rmal.2025.100235
Ruirui Jia , Ekaterina Sudina , Kejun Du
{"title":"Do data collection methods matter for self-reported L2 individual differences questionnaires? In-person vs crowdsourced data","authors":"Ruirui Jia ,&nbsp;Ekaterina Sudina ,&nbsp;Kejun Du","doi":"10.1016/j.rmal.2025.100235","DOIUrl":"10.1016/j.rmal.2025.100235","url":null,"abstract":"<div><div>Crowdsourcing offers great advantages in data collection by enabling researchers to recruit a large number of participants across geographical boundaries within a short period of time. Despite the benefits of crowdsourcing, no study has explored its validity in collecting self-reported individual differences (ID) data in second language (L2) research. The present study aims to address this gap by examining crowdsourcing as a viable alternative or complementary tool to traditional in-person data collection. We recruited a total of 209 in-person and 209 crowdsourced participants for comparison. Both groups completed the short versions of the Foreign Language Classroom Anxiety Scale and the Foreign Language Enjoyment Scale, provided their demographic and language learning background information, and completed the LexTALE test. Measurement invariance testing revealed that most (sub)constructs exhibited partial or full invariance, indicating stability in the measurement systems across both data collection settings. However, crowdsourced participants reported higher enjoyment and lower anxiety than in-person participants. These differences can be attributed to the more relaxed mental state of the crowdsourced participants who completed the survey outside of the classroom. Moreover, some crowdsourced participants tended to overrate their English proficiency and exhibited potentially dishonest behavior during the LexTALE test. These findings suggest that although crowdsourcing offers valuable opportunities for data collection in L2 ID research, the potential for inflated self-assessments and questionable behavior in an unsupervised online testing environment must be considered. Thus, the use of crowdsourcing platforms to collect self-reported L2 ID data requires caution and careful preparation.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100235"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Oral task repetition research via videoconferencing 基于视频会议的口语任务重复研究
Research Methods in Applied Linguistics Pub Date : 2025-07-03 DOI: 10.1016/j.rmal.2025.100232
Joe Kakitani
{"title":"Oral task repetition research via videoconferencing","authors":"Joe Kakitani","doi":"10.1016/j.rmal.2025.100232","DOIUrl":"10.1016/j.rmal.2025.100232","url":null,"abstract":"<div><div>A substantial body of research has demonstrated the benefits of oral task repetition in enhancing second language (L2) performance. However, empirical studies investigating its effects on L2 development through longitudinal designs remain limited. This limitation may be partly due to the methodological challenges of traditional classroom- and laboratory-based research, such as participant attrition and scheduling difficulties. This paper explores the potential of online oral experimentation via videoconferencing—experiments conducted through synchronous computer-mediated communication using platforms like Zoom and Microsoft Teams—to advance L2 oral task repetition research. After reviewing research on task repetition and the methodological characteristics of conventional classroom- and laboratory-based studies that may present challenges within this domain, this article discusses the advantages of online experiments conducted via videoconferencing, including greater convenience and flexibility, increased efficiency, improved control of extraneous factors, and automated speech transcription. In addition, it examines the ecological validity of online video-based oral experiments. Methodological recommendations are also provided to help researchers address some of the challenges associated with conducting experiments via videoconferencing.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Leading a scoping review on L2 pronunciation: Some key elements of methodology 领导二语发音的范围审查:方法论的一些关键要素
Research Methods in Applied Linguistics Pub Date : 2025-07-02 DOI: 10.1016/j.rmal.2025.100216
Linda Terrier , Marie Garnier , Saandia Ali
{"title":"Leading a scoping review on L2 pronunciation: Some key elements of methodology","authors":"Linda Terrier ,&nbsp;Marie Garnier ,&nbsp;Saandia Ali","doi":"10.1016/j.rmal.2025.100216","DOIUrl":"10.1016/j.rmal.2025.100216","url":null,"abstract":"<div><div>This article describes the methodology of a scoping review covering 25 years of research on L2 English pronunciation. We focus on two key methodological steps required in any scoping review: identifying the information source and selecting the studies. We present a rationale for employing a manual search across prominent journals in the fields of phonetics and phonology, second language acquisition, and second language learning and teaching. We describe how we delineated the scope of the review by identifying 35 prominent journals and how we organized teamwork to select relevant studies. We show that seemingly straightforward inclusion criteria (L2 English, empirical research, and pronunciation) raise questions about the objects of study in the field. The final corpus includes 463 articles published in the 35 identified journals between 1996 and 2020. We demonstrate that Arksey and O’Malley’s framework for scoping reviews can be applied and adapted to the specificities of L2 English pronunciation research, but we also highlight the challenge of iterativity in study selection. As we present the distribution of articles over time and across journals, we make recommendations for future scoping reviews regarding the time span of the review and the identification of the initial information source. In particular, the <em>Journal of Second Language Pronunciation</em>, which stands out as a central venue for L2 English pronunciation research, would have been missed had we used a more typical keyword search across academic databases.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100216"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Predicting the CEFR level of English listening texts with machine learning methods 用机器学习方法预测英语听力文本的CEFR水平
Research Methods in Applied Linguistics Pub Date : 2025-07-01 DOI: 10.1016/j.rmal.2025.100234
Christopher Robert Cooper
{"title":"Predicting the CEFR level of English listening texts with machine learning methods","authors":"Christopher Robert Cooper","doi":"10.1016/j.rmal.2025.100234","DOIUrl":"10.1016/j.rmal.2025.100234","url":null,"abstract":"<div><div>Comprehension in listening texts is often judged by lexical coverage. However, this might not be easily interpretable for language teachers. The CEFR is becoming increasingly influential due to its standardized descriptors across languages. Learners are often placed into classes based on proficiency level, therefore a CEFR level is likely more interpretable than lexical coverage when judging listening text difficulty. Machine learning methods have been used to predict the CEFR level of English reading texts and learner writing, but no such studies exist for listening. The current study hopes to bridge this gap by investigating the potential to predict the CEFR level of listening texts. A corpus of CEFR-labelled listening texts (728 texts, 345,104 words) was compiled for text classification. Three types of variables were created from the corpus data to evaluate comparative predictive accuracy. The first method used linguistic and acoustic features. The others used text embeddings, which represent semantic meaning. The data was split into four classes: A1, A2, B1, and B2+. The accuracy of each method was evaluated by comparing the predicted label in the test data with the label from the original text. The most accurate method used OpenAI embeddings and Support Vector Machines. The overall accuracy was 0.81, with macro averages of precision = 0.75, recall = 0.78, and f-score = 0.76, indicating balanced classification performance across CEFR levels. This method has the potential to predict the CEFR level of listening texts, which could help practitioners and researchers match learners and participants to appropriate listening texts.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Evaluating the linguistic complexity of machine translation and LLMs for EFL/ESL applications: An entropy weight method 评估机器翻译和llm在EFL/ESL应用中的语言复杂性:一种熵权法
Research Methods in Applied Linguistics Pub Date : 2025-06-30 DOI: 10.1016/j.rmal.2025.100229
Yingqi Huang, Dechao Li, Andrew K.F. Cheung
{"title":"Evaluating the linguistic complexity of machine translation and LLMs for EFL/ESL applications: An entropy weight method","authors":"Yingqi Huang,&nbsp;Dechao Li,&nbsp;Andrew K.F. Cheung","doi":"10.1016/j.rmal.2025.100229","DOIUrl":"10.1016/j.rmal.2025.100229","url":null,"abstract":"<div><div>English as a Foreign and Second Language (EFL/ESL) learners are increasingly using machine translation (MT) tools such as neural machine translations (NMTs) and large language models (LLMs) to enhance their language learning and translation processes due to their accuracy and efficiency in both cost and time compared with human translation. Given the distinct linguistic features exhibited by NMTs and LLMs, it is crucial to assess the linguistic complexity of texts produced by these tools to optimize their use in EFL/ESL teaching and learning. This study examines two forms of absolute linguistic complexity, namely lexical complexity and syntactic complexity, that influence EFL/ESL activities. Lexical complexity affects vocabulary recognition and semantic processing, while syntactic complexity influences sentence parsing and the internalization of grammatical rules. As both dimensions are multi-faceted and involve numerous indices that may vary in different directions (e.g., high values in certain measures and lower in others), an entropy weight method (EWM) is employed to assign data-driven weights and derive a balanced holistic complexity score. This approach enables a systematic comparison of translation outputs from NMTs (Google Translate, DeepL) and LLMs (ChatGPT-4o, OpenAI-o1). The findings reveal that LLMs generally exhibit higher holistic linguistic complexity, whereas NMTs tend to produce simpler translations. Pedagogically, LLM-translated texts may serve as more effective input for advanced language learners in EFL/ESL contexts, while NMT outputs may be more suitable for those with less linguistic proficiency.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Bayesian approach to small samples: Mixed-effects modeling in L2 interventional research 小样本贝叶斯方法:L2介入性研究中的混合效应建模
Research Methods in Applied Linguistics Pub Date : 2025-06-27 DOI: 10.1016/j.rmal.2025.100231
Man Ho Ivy Wong
{"title":"A Bayesian approach to small samples: Mixed-effects modeling in L2 interventional research","authors":"Man Ho Ivy Wong","doi":"10.1016/j.rmal.2025.100231","DOIUrl":"10.1016/j.rmal.2025.100231","url":null,"abstract":"<div><div>Small sample sizes are a common challenge in second language (L2) research, particularly in classroom-based studies or exploratory intervention work. Traditional frequentist approaches often lack the flexibility needed to analyse such data meaningfully. This paper presents a two-study Bayesian tutorial designed to address the small-N problem using logistic mixed-effects models. In Study 1<strong>,</strong> we analyse pilot data from 27 final-year or postgraduate students across three instructional conditions, using Bayesian mixed-effects modelling with non-informative (uniform) priors to explore effects of instruction, time, conditional type, and proficiency on participants’ binary responses in two language assessment tasks (a processing test and a production test). In Study 2, we build on the pilot by modelling follow-up data from a refined version of the study, focusing on the one treatment group only. Here, we incorporate highly informed priors derived from the posterior estimates of Study 1, demonstrating how prior information can improve estimation and interpretability, even with small datasets. This paper offers practical guidance on specifying priors, modelling binary outcomes, and applying Bayesian reasoning across iterative L2 research designs.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Language without borders: A step-by-step guide to analyzing webcam eye-tracking data for L2 research 语言无国界:一步一步的指导分析网络摄像头眼动追踪数据的第二语言研究
Research Methods in Applied Linguistics Pub Date : 2025-06-24 DOI: 10.1016/j.rmal.2025.100226
Jason Geller , Yanina Prystauka , Sarah E. Colby , Julia R. Drouin
{"title":"Language without borders: A step-by-step guide to analyzing webcam eye-tracking data for L2 research","authors":"Jason Geller ,&nbsp;Yanina Prystauka ,&nbsp;Sarah E. Colby ,&nbsp;Julia R. Drouin","doi":"10.1016/j.rmal.2025.100226","DOIUrl":"10.1016/j.rmal.2025.100226","url":null,"abstract":"<div><div>Eye-tracking has become a valuable tool for studying cognitive processes in second language acquisition and bilingualism (Godfroid et al., 2024). While research-grade infrared eye-trackers are commonly used, several factors limit their widespread adoption. Recently, consumer-based webcam eye-tracking has emerged as an attractive alternative, requiring only a personal webcam and internet access. However, webcam-based eye-tracking introduces unique design and preprocessing challenges that must be addressed to ensure valid results. To help researchers navigate these challenges, we developed a comprehensive tutorial focused on visual world webcam eye-tracking for second language research. This guide covers key preprocessing steps—from reading in raw data to visualization and analysis—highlighting the open-source R package webgazeR (Geller, 2025), freely available at: <span><span>https://github.com/jgeller112/webgazer</span><svg><path></path></svg></span>. To demonstrate these steps, we analyze data collected via the Gorilla platform (Anwyl-Irvine et al., 2020) using a single-word Spanish visual world paradigm (VWP), showcasing evidence of competition both within and between Spanish and English. This tutorial aims to empower researchers by providing a step-by-step guide to successfully conduct webcam-based visual world eye-tracking studies. To follow along, please download the complete manuscript, code, and data from: <span><span>https://github.com/jgeller112/L2_VWP_Webcam</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A systematic examination of generative artificial intelligence (GenAI) use guidelines in applied linguistics journals 应用语言学期刊中生成式人工智能(GenAI)使用指南的系统研究
Research Methods in Applied Linguistics Pub Date : 2025-06-20 DOI: 10.1016/j.rmal.2025.100227
Shuhui Yin, Carol A. Chapelle
{"title":"A systematic examination of generative artificial intelligence (GenAI) use guidelines in applied linguistics journals","authors":"Shuhui Yin,&nbsp;Carol A. Chapelle","doi":"10.1016/j.rmal.2025.100227","DOIUrl":"10.1016/j.rmal.2025.100227","url":null,"abstract":"<div><div>The unannounced appearance of GenAI in 2022 and the speed of its adoption by researchers have left many questions unanswered about its accepted ethical use, with no apparent consensus among applied linguists. In this context, it’s essential for researchers to develop their GenAI literacy for research to engage with GenAI effectively and responsibly. This study contributes to identifying key components of this literacy through examining accepted GenAI uses in research practices. Based on a systematically sampled collection of 170 high-impact journals in applied linguistics, we investigated the scope and nature of GenAI use guidelines provided by 76 journals intended to guide authors. A checklist including four items regarding general statements and 17 items regarding three categories of specific aspects that GenAI guidelines target (authorship, uses, and human responsibility) was identified. Our findings reveal that (1) less than half of the journals provided GenAI use guidelines to guide authors, (2) the number of specific aspects varied across journals, with most falling short of comprehensive coverage, and (3) disagreements were observed about whether AI can be cited and used for manuscript drafting, idea generating, image generating, data generation, data collection, and data analysis and interpretation. Additionally, journals varied in their guidance on how to disclose GenAI uses. We propose recommendations for journals in improving their AI guidelines. Importantly, we introduce and conceptualize the new construct GenAI literacy for research article writing (GenAI-LR) that is important for authors to develop. We provide actionable recommendations accordingly based on our findings.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"4 3","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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