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Writing an abstract is a challenging assignment for graduate students as it requires condensing all the extensive research into a few sentences, providing sufficient background knowledge, and presenting findings compellingly to the academic community. This study observes how graduate engineering students cope with writing their abstracts for their first published papers, with a specific focus on metadiscourse. The study is based on the learner corpus of 1,746 abstracts (117,535 words) written by non-native English speakers in English and Serbian during their Master's studies. The research follows Hyland's taxonomy, focusing on interactive frame markers and interactional engagement markers, together with metadiscursive nouns in order to uncover cross-linguistic patterns and pedagogical implications. The comparison of the absolute and relative frequency with statistical significance and log likelihood between Serbian and English sub-corpora demonstrates that students tend to use frame markers with greater frequency in Serbian abstracts than in English ones. Additionally, engagement markers, and especially directives, are used twice as often as frame markers in both sub-corpora, with a higher prevalence in English abstracts. Following the qualitative and quantitative analyses, the findings offer pedagogical implications related to the range of frame markers and metadiscursive nouns used by students to introduce their research aims and the range of engagement markers used to engage readers in their research and thus claim their credibility in academic writing. [ABSTRACT FROM AUTHOR]

Background and Purpose: With the rapid advancements in generative AI, understanding its ability to emulate human language conventions is crucial. This work aims to analyze the possibility of applying AI technology in language production by comparing the lexico-grammatical features of abstracts created with the help of ChatGPT and written by British and American researchers. Methodology: Twenty papers written by researchers affiliated with UK universities and twenty by researchers affiliated with American universities were selected from the journals listed under the first quartile of the Web of Science and Scopus. Using the titles that were from the selected works, 40 abstracts were generated from ChatGPT for comparison. Each article was introduced with its title, and ChatGPT was asked to create an abstract based on the title. Subsequently, the subjects were examined with the help of Biber’s (1991) multivariate model considering five dimensions, which include the Informational vs Involved discourse, the Narrative vs Non-narrative, the Explicit vs. Situation- dependent discourse, Overt expression of argumentation/persuasion and the Impersonal/Abstract style as opposed to the Non impersonal/Non-abstract style. Findings: The five factors analysed in the texts give evidence that ChatGPT generates more information centric, non-narrative, argumentative, and less abstractive discourse than human researchers. Contributions: The results of the study show the possibilities for the further development of AI that helps to create language closer to human language. [ABSTRACT FROM AUTHOR]

The study of research abstracts has gained significant scholarly attention as part of genre studies due to the communicative importance of abstracts in constructing academic knowledge. This study contributes to the discussion by examining the structural organization and lexico-grammatical features of ninety (90) postgraduate theses abstracts in the disciplines of English Language studies, Literature studies, and Linguistics studies (Ghanaian Languages). The abstracts were purposively sampled from the graduate theses of departments of the School of Languages, the University of Ghana. The data was analyzed using Hyland's (2000) genre model of research abstracts. The findings reveal that abstracts of English Language studies and Linguistics studies (Ghanaian Languages) are often informative while Literature studies abstracts are more indicative and possess distinct structuring of the moves. The Purpose move (M2) remains obligatory and the Conclusion move (M5) is optional across the disciplines. Also, some lexico-grammatical features in the linguistic choices of scholars in the three disciplines point to evidential differences that mark informative abstracts as varying from indicative abstracts. The study concludes that identifying the discipline-specific function of the abstracts may be the best means to account for variations in abstracts of varying disciplines and calls for the deliberate enculturation of academics into discipline-oriented research writing skills to improve the presentation of research ideas in abstracts. [ABSTRACT FROM AUTHOR]

Background and objective: Prior studies reveal that invited speaker panels, editorial boards, authors of practice guidelines, and senior authors of published articles are disproportionately male in the neurology field. We aimed to analyze a gender gap in authorship of accepted abstracts to the American Academy of Neurology annual meetings in 2020 and 2021. Design/methods: This is a cross-sectional study evaluating the proportions of female first and senior abstract authors in 2020 and 2021. Abstracts were reviewed manually (n = 3,211 in 2020; n = 2,178 in 2021). Data were collected regarding the gender of first and senior authors, subspecialties, and origin of research (USA, international, or corporate-affiliated). Then, we compared the percentages of female first and senior authors in the 2 years to assess for any short-term effects of the COVID-19 pandemic. Results: Accepted abstracts with female first and senior authors comprised 46%, 34% in 2020, and the same in 2021, without change. Female senior authors had a significantly higher proportion of female first authors than their male senior author counterparts. The analysis of subspecialties with more than 100 abstracts showed the lowest percentages of female senior authors was oncology (24.7%), sleep (25.5%), headache (28.7%), and cerebrovascular disease (29%) in 2020. Cerebrovascular disease (29%) and behavioral neurology (24.7%) had the lowest percentage of female senior authors in 2021. In the analysis of the origin of research, corporate-affiliated authors had the lowest percentages of female first (34 and 36%) and senior authors (22.6 and 27.6%). Conclusion: The gender gap in neurology was reaffirmed in regards to female senior authorship overall and in subgroups of abstracts including cerebrovascular disease, headache, behavioral neurology, sleep, oncology, and corporate-affiliated research. [ABSTRACT FROM AUTHOR]

The present study aims to explore the use of meta-discourse markers in abstract and conclusion sections of 20 theses submitted by M. Phil English students of Qurtuba University D. I. Khan, Pakistan. Meta-discourse markers in communication and academic writing have become the chief concern in recent years. This research utilisez Hyland's model of meta-discourse markers that help to create an understanding for readers to explore the genre, social and contextual backgrounds of text in the domain of academic writing. The mixed-method approach was used to explore the most frequent metadiscourse categories as well as the deeper meaning behind the construction of language. The exploration came in the form of sentences and examples to negotiate the non-native writers' use of meta-discourse. It was found that the members who have gone through the treatment of language approach were in a situation in fostering their insight with regards to lexical collocation. The implications of this research extend beyond a mere exploration of linguistic nuances; understanding how meta-discourse functions in the context of the abstract and conclusion sections can offer valuable insights into the rhetorical strategies employed by authors to convey the significance of their research and summarize key findings. [ABSTRACT FROM AUTHOR]