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In this study, social media addiction, which attracts the attention of researchers today and is a risk factor for adolescents, was examined as a predictor of subjective well-being, which is accepted as one of the dimensions of functionality for mental health, especially within the framework of a positive approach. While explaining this relationship, the variables of social self-efficacy and school engagement, which again contribute to the psycho-social development area of adolescence as a supporter, were also included in the process as mediator variables. The sample of the study consists of 821 students studying in high schools. SPSS 27.0 program was used for the correlation analyses of the estimated model of the study, and PROCESS Macro (Model-6) was used for mediator analyses. As a result of the analyses, it was seen that level of social media addiction was negatively and slightly significantly related to subjective well-being and social self-efficacy and school engagement, which were included as mediator variables. Correlation findings showed that subjective well-being was also positively and moderately significantly related to social self-efficacy and school engagement. It was also seen that there was a positive and moderately significant relationship between the mediator variables of the study (social self-efficacy and school engagement). Finally, in the analysis of the study, the indirect effect of social media addiction’s level on subjective well-being was evaluated through both social self-efficacy and school engagement, and the relationship was found to be significant.
This study aimed to assess the possibility of developing a dairy population of Akkaraman sheep following a long-term within-breed phenotypic selection program in semi-arid conditions in Türkiye. The study was conducted at Gözlü State Farm between 2012 and 2023 (12 years), covering approximately 3–4 generations, and included milk yield records from 6,822 purebred Akkaraman ewes. Milk yields were recorded on days 1, 45, 60, 75, 90, 105, and 120 of lactation. Quadratic spline interpolation based on these test-day means was used to reconstruct daily milk yield and to standardize lactation performance over a 120-day period; no formal genetic parameter estimation (e.g., BLUP, REML) was applied. Across the 12-year study period, the mean lactation milk yield reached 119.15 kg, with a mean daily milk yield of 0.99 kg, representing approximately a 2.5-fold increase compared with historical reports for the Akkaraman breed. The lactation curve exhibited a gradual peak followed by a sustained production phase, indicating a shift toward a more dairy-type lactation pattern. The results indicate that continuous within-breed phenotypic selection significantly enhances milk production in a fat-tailed, dual-purpose sheep breed without crossbreeding while preserving adaptation to semi-arid production conditions. The findings show that local sheep breeds can help create environmentally friendly and sustainable dairy production in areas with limited resources.
This study aimed to develop and validate the Text-Based Generative AI Literacy Scale (T-GASE)—a multidimensional instrument designed to assess individuals’ competencies in interacting with, evaluating, and ethically engaging with text-based Generative AI (GenAI) tools. The scale development followed Hinkin’s five-step framework, including item generation, expert validation, pilot testing, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). A total of 645 undergraduate students participated in the study, with 332 students in the EFA phase and 313 in the CFA phase, all from diverse academic disciplines such as engineering, education, architecture, economics, and social sciences. Grounded in four well-established theoretical frameworks—the Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM), Expectancy-Value Theory (EVT), and Diffusion of Innovations Theory (DIT)—the final 23-item scale revealed a robust four-factor structure: Application, Expectations, Ethics, and Evaluation. The scale demonstrated strong psychometric properties, with high internal consistency and excellent model fit indices. Results showed that students had high expectations for the future of GenAI and were actively using it but exhibited lower confidence in critically evaluating AI-generated content. Moreover, students from STEM-oriented disciplines outperformed those from non-STEM fields, suggesting disciplinary differences in GenAI literacy levels. Unlike existing AI literacy scales that broadly assess technical knowledge or general attitudes, the T-GASE uniquely addresses the practical, ethical, and evaluative skills specific to text-based GenAI tools, including prompt crafting, misinformation detection, and adaptive usage. These findings highlight the T-GASE as a pioneering and theoretically grounded instrument for assessing GenAI literacy, with significant implications for curriculum design and digital competence development across higher education contexts.
Boron is an essential trace element with emerging physiological significance; however, its effects on the female reproductive system remain poorly characterized. Most available studies are limited to single-dose applications or short-term exposures. In this study, we investigated the dose and duration dependent effects of oral boron exposure on ovarian follicular reserve, apoptosis, and reproductive hormone profiles in female Wistar albino rats. Animals were assigned to low- and high-dose borax groups (1250 and 5000 mg/kg/day, respectively) and exposed for short-term (7 and 15 days) or long-term (30 and 60 days) periods. At the end of each exposure period, ovarian tissues were collected for histomorphometric evaluation, while apoptosis was assessed using the TUNEL assay. Serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), and anti-Müllerian hormone (AMH) were also measured. Histomorphometric analysis revealed a significant reduction in primordial and primary follicle numbers, indicating depletion of the ovarian reserve, particularly in the high-dose and long-term exposure groups. TUNEL analysis demonstrated a marked increase in apoptotic cell density in ovarian tissues following prolonged boron administration. In parallel, boron exposure induced significant dose and duration dependent alterations in circulating reproductive hormone levels. Collectively, these findings suggest that chronic and high-dose oral boron exposure adversely affects ovarian morphology and function through enhanced apoptotic activity and disruption of endocrine homeostasis. These results highlight the potential reproductive toxicity of long-term boron exposure and underscore the need for further molecular studies to clarify the underlying mechanisms and to evaluate the translational relevance of these effects for human female reproductive health.
This study investigates position bias in ChatGPT’s scoring patterns for automated essay scoring, with a focus on primacy and recency effects. Position bias, originating from the serial position effect in cognitive psychology, refers to the tendency of Large Language Models (LLMs) to emphasize the introduction and conclusion of a text while potentially neglecting content in the middle sections. Using 192 synthetic essays across varying lengths and section qualities, this research explores whether ChatGPT disproportionately weighs the quality of introductions and conclusions compared to body paragraphs. Statistical analyses reveal that while ChatGPT successfully differentiates between strong and weak sections, no consistent evidence supports the presence of systematic primacy or recency effects in overall scoring. Domain-specific analyses further indicate that rubric categories such as grammar and mechanics are sensitive to errors throughout essays, while content and organization are more heavily influenced by body quality. The findings suggest that ChatGPT’s scoring patterns are largely balanced, with minimal signs of position bias, thereby enhancing the validity of its use for automated scoring. This research highlights the need for continued evaluation of AI-based grading systems to ensure fairness and reliability while proposing avenues for future exploration in LLM-driven assessments.
