فصلنامه تحقیقات کتابداری و اطلاع رسانی دانشگاهی
سال پنجاه و هشتم شماره 3 (پیاپی 109، پاییز 1403)

In the era of digital transformation, libraries play a pivotal role in facilitating access to information. This research aims to develop a comprehensive framework for enhancing digital information dissemination services at the National Library and Archives of Iran. By addressing the limitations of existing studies, this work contributes to the field by offering a novel perspective on the essential components and indicators for effective digital information dissemination.

A mixed-methods approach was employed. The qualitative phase utilized meta-synthesis to identify potential components and indicators. Subsequently, a Delphi study was conducted to validate and refine these elements through expert consensus. The study population consisted of information dissemination experts, 16 of whom agreed to participate and were selected as members of the Delphi panel.

The Delphi study identified seven critical components: research documentation, information resources, collection development, standardization, information services, dissemination tools, and evaluation. Each component includes specific indicators essential for optimizing digital information dissemination services. Based on the data analysis, the components and indicators for digital information services were developed.

The results confirmed all identified components and indicators. This research provides a valuable framework for libraries to improve their digital services and meet evolving user needs. By implementing these components and indicators, the National Library and Archives of Iran can optimize its information dissemination processes and foster knowledge creation and innovation. These components and indicators assist libraries and information organizations in improving information dissemination and meeting the information needs of their users..

The purpose of this research was to investigate the types of informal collaborations within the acknowledgments section of articles in the field of chemistry.

This research is a quantitative study employing a scientometric approach and based on data mining techniques, utilizing library research and descriptive methods. The statistical population of the study consists of open-access research articles from the Web of Science database. The Stanford Named Entity Recognizer software was used for data mining and extraction of the acknowledgments section of the articles.

The research findings indicated that acknowledgments in chemistry articles, based on Hyland's three-level model of acknowledgment text structure, are dedicated to acts of thanking, acts of reflection, and acts of informing, respectively. Furthermore, the Chinese Academy of Sciences, the University of California, Berkeley, and the Massachusetts Institute of Technology (MIT) were the organizations and universities that received the most acknowledgments. The citation rate is higher in articles that acknowledge more than two individuals (the average number of acknowledged individuals) compared to other articles. In addition, there was a significant relationship between the number of individuals acknowledged in an article and the number of citations the article received.

Considering that acknowledgments can impact citation rates, raising awareness among individuals and persuading them of the importance of citation in shaping personal and institutional identity and branding can be effective by institutionalizing a sense of loyalty and gratitude. Moreover, perhaps one reason for the weak correlation between citation rates and acknowledgments is the lack of consistency in the names of individuals and organizations, as well as the location of acknowledgments in most articles, which is at the end of the article.

 The present study aims to identify the features necessary for developing an e-book reading application designed specifically for children with dyslexia, enabling them to engage more effectively with storybooks.

A systematic review method was employed, following the model proposed by Kitchenham and Charters to ensure coherence in data collection. Relevant texts were gathered by searching for related keywords in both national and international databases, retrieving 216 studies (32 in Persian and 184 in English). In the initial review, unrelated, duplicate, and invalid studies were removed by examining the articles’ titles, abstracts, and results. In the next step, two experts in Library and Information Science validated and screened the studies. Ultimately, 74 studies in English and 10 studies in Persian (totaling 84 studies) were selected as the basis for analysis.

The systematic review identified four overarching categories of features: search, profile, book, and user interface. Additionally, 12 organizing features were extracted, including search techniques, features in the search results list, user's personal information, my library panel, my activities panel, personalization panel, descriptive information about the book, electronic display of the book, image features, tools to assist in better reading of the book, and specific user interface features for displaying books to children with reading difficulties. Furthermore, 180 foundational features for e-book reading applications were identified.

The features extracted in this research can be divided into general and specific features. General features include those related to search (such as search techniques and capabilities available in the results list), profiles of children with dyslexia (including user personal information, my library panel, my purchases panel, my activities panel, and personalization), and the user interface of the system. These features should be considered in all reading applications designed for children. On the other hand, specific features are tailored to reading applications for children with dyslexia and are implemented by taking into account the unique needs of this group. Features related to the electronic display of books, book images, and tools that assist in better reading of books fall into this category.

Research competence is one of the most significant and influential factors among postgraduate students. Therefore, this study aims to investigate the mediating role of research motivation in the relationship between information literacy and research competence among graduate students at Payam Noor University in Kermanshah and Kurdistan.

This is a descriptive-analytical, correlational study that examines the relationship between dependent, independent, and mediating variables using variance-based structural equations. The research population consisted of 1,200 graduate students from Payame Noor Universities in Kermanshah and Kurdistan provinces in Iran, of which 270 were selected as the sample. Data collection tools included questionnaires: the research competence questionnaire by Yonsi et al. (2016), the research motivation questionnaire by Salehi et al. (2015), and the information literacy questionnaire by Yazdani (2015). SPSS software (version 22) was used for data analysis, and SmartPLS software was employed to assess the fit of the research model.

The research findings revealed that information literacy has a positive and direct effect on research motivation, and research motivation, in turn, has a positive and direct effect on research competence. Additionally, information literacy directly and positively influences research competence. Furthermore, research motivation plays a mediating role in the relationship between information literacy and research competence.

Today, research skills are essential for graduate students. According to the results of this study, strengthening the information literacy and research motivation of graduate students can positively contribute to the development of their research skills.

The current research aims to examine the methodological frameworks employed in scholarly articles on information literacy. Based on these frameworks, it seeks to propose a new model for information literacy studies to contribute to methodological development.

In the present study, a mixed-method framework was used to achieve the results. The research population consisted of 682 Persian and English articles in the field of information literacy published between 1992 and 2023. Using theoretical sampling, articles with methodological originality were selected, while similar or redundant articles were excluded. Ultimately, 193 articles were chosen as the sample for analysis. First, through a categorization process, the studies in the field of information literacy were divided into three approaches: quantitative, qualitative, and mixed. Next, thematic analysis was used to identify the methodological frameworks of the selected articles, and their distinct methodological ideas and themes were extracted. Finally, a model for the methodological development of studies in this field was proposed, incorporating Roy Bhaskar's ideas and the concept classification method.

Based on the research's output model, the methodological frameworks were categorized into three levels: empirical knowledge, procedural knowledge, and perceptual knowledge. The level of empirical knowledge is considered the foundational level, encompassing basic methodologies. It generally includes methods used to gain an understanding of the general context of the subject under study, such as surveys, experimental methods, Delphi studies, and similar methodological frameworks. The level of procedural knowledge represents the intermediate level of information literacy research, primarily focusing on investigating the processes, steps, patterns, and procedures involved in acquiring information literacy. This level goes beyond empirical knowledge by examining the steps, stages, and processes, as well as how literacy is achieved. The level of perceptual knowledge is the third and highest level of information literacy research. At this level, the researchers aim to study contradictions, conflicts, and the dialectical interactions among actors in obtaining information, as well as the structures that limit access.

Research in the field of information literacy, considering the methods employed thus far and in light of the Bhaskar model, should ideally begin with empirical studies, proceed to procedural studies, and conclude with interrogative studies. The one-dimensional nature of research in this field, along with the failure to follow this sequence, often leads to fragmented outcomes and a lack of data enrichment throughout the process. This, in turn, diminishes the effectiveness and impact of the findings

This study investigates the role of large language models (LLMs) in detecting logical fallacies during the peer-review process, aiming to improve the accuracy, transparency, and reliability of scientific publications. Additionally, the research evaluates the potential of LLMs to reduce the workload on human reviewers and standardize evaluation practices.

The research involved a series of experiments designed to evaluate the ability of advanced language models, such as ChatGPT (versions 4 and o1), to identify and classify logical fallacies, solve reasoning problems, and analyze academic texts of varying lengths and complexities. Standard datasets, including the ElecDeb2060 dataset and logic questions from the Iranian Ph.D. Entrance Exam, were used. Classical machine learning models, including Support Vector Machine (SVM) and Random Forest, were employed as baseline comparisons. Advanced optimization techniques and zero-shot learning approaches were applied to prepare the language models for the analyses.

The results demonstrated the exceptional performance of advanced language models, particularly ChatGPT o1, which achieved 98.1% accuracy in detecting logical fallacies and 100% accuracy in solving logic problems from the Ph.D. Entrance Exam. In contrast, classical machine learning models, such as SVM and Random Forest, recorded significantly lower accuracies of 48% and 49%, respectively. Other advanced models, such as Mistral and LLama, exhibited moderate performances, with accuracies ranging from 76% to 78.5% in identifying logical fallacies. For longer and more complex texts, ChatGPT o1 maintained 100% accuracy in identifying and naming fallacies, while other models demonstrated reduced capabilities, with accuracies below 50%.In addition to their accuracy, the advanced LLMs displayed a remarkable ability to analyze complex arguments, identify subtle logical errors, and provide structured feedback. These features highlight their potential for improving both the efficiency and the quality of the peer-review process by reducing human error and offering detailed, objective evaluations.

Large language models, particularly ChatGPT o1, have shown substantial potential to redefine traditional peer-review practices. These models can enhance the speed, precision, and transparency of evaluations, thereby supporting the publication of high-quality research articles. By identifying logical fallacies and cognitive biases, they offer structured feedback that aids authors in refining their work and ensures the integrity of scientific literature. However, human reviewers remain essential as final arbiters in the process, ensuring a balanced integration of AI's analytical capabilities with human expertise. This synergy can pave the way for a more robust, efficient, and transparent peer-review system, fostering progress in scientific research.