Jacksonville State University

This commentary asks what the French Revolution can teach social work about practicing ethics amid democratic unravelling in the contemporary US. Using historical comparative political analysis grounded in deontological and intersectional lenses, it traces parallels between revolutionary France and current struggles over security, dissent, and civil liberties. The article argues that institutional risk aversion and political repression are pushing social workers in academia, child welfare, and community nonprofits toward complicity. While the code of ethics of the National Association of Social Workers names professional duties, the manifesto of the Social Work Action Network and the Dubrovnik Manifesto illustrate how to collectivize them in practice. The article proposes seven evidence-informed strategies as a roadmap for democratic, justice-oriented practice: protected ethical consultation and supervision; advocacy networks; creative and covert resistance; documentation of harm; practices that sustain worker well-being; decision making grounded in radical theory; and community-led action. The social work profession must function as an organized democratic resistance.

ABSTRACT This study analyzes 224 verified Amazon consumer reviews of BDSM gear to explore how erotic pain, power exchange, gender, and embodiment are experienced and narrated. Through qualitative content analysis, three central themes emerged: Pleasure and Pain, Control and Surrender, and Identity and Transformation. Reviews of products marketed to men emphasize endurance, technical mastery, and dramatized submission, while reviews of products marketed to women highlight relational intimacy and visually expressive play.

Wright, WC, Aguiar, EJ, Winchester, LJ, Fedewa, MV, and Esco, MR. Agreement between a motorized resistance device and traditional systems for sprint and change-of-direction testing in female volleyball players. J Strength Cond Res 40(2): 198-205, 2026-The purpose of this study was to determine the agreement between a motorized sprint resistance device (MSRD) and timing gates and an inertial measurement unit (IMU), for assessing time to completion and peak velocity during linear and change-of-direction tasks in elite female volleyball players. Elite volleyball athletes ( n = 16, 19.4 ± 1.5 years, 176.2 ± 10.6 cm, 71.5 ± 11.1 kg) performed the 18.29 m sprint and 5-10-5 drills while being simultaneously assessed using an MSRD, timing gates, and an IMU. Compared with timing gates for measuring sprint (3.09 ± 0.16 seconds) and 5-10-5 (5.08 ± 0.19 seconds) time, the MSRD showed significant ( p < 0.01) mean differences (3.15 ± 0.17 seconds [ p < 0.01, Cohen's d = 0.49, small ]) and 5.16 ± 0.21 seconds [ p < 0.01, Cohen's d = 1.14, moderate ], respectively, but the correlations were very large to nearly perfect ( r = 0.97 and 0.87, respectively). Limits of agreement (LOA) for sprint time were -0.10 ± 0.10 seconds and for 5-10-5 time were 0.23 ± 0.24 seconds. For peak velocity, the mean values from MSRD and IMU for the sprint (4.62 ± 0.34 m·s -1 and 4.72 ± 0.44 m·s -1 , respectively [ p = 0.78]) and 5-10-5 drill (3.91 ± 0.47 m·s -1 and 3.93 ± 0.50 m·s -1 , respectively [ p = 0.98]) were not significantly different. The correlation between the peak velocity values was significant for the sprint ( r = 0.65, p < 0.01), but not for 5-10-5 ( r = 0.25, p = 0.69), with LOA values of 0.03 ± 0.86 m·s -1 and 0.00 ± 0.98 m·s -1 , respectively. The MSRD seems to agree with timing gates for sprint and 5-10-5 time. However, the agreement with IMU for peak velocity is weaker.

As artificial intelligence (AI) continues to redefine the boundaries of linguistic research, this study examines the extent to which machine translation (MT) and AI tools can go beyond literal meaning, push beyond surface-level syntax and semantics to process context-sensitive issues in literary translation. While traditional MT systems such as Google Translate and Microsoft Translator are optimized for direct source-to-target mapping, AI language models like ChatGPT represent a broader category of tools designed for general-purpose language generation, including but not limited to translation. Using a 14-line Arabic poem, translations were generated by three MT systems, one AI model (ChatGPT), and two certified human translators. These outputs were evaluated against ten linguistic and stylistic dimensions: punctuation, layout, rhyme, mood, theme, logico-semantics, transitivity, field, tenor, and mode. The six translation versions were compared using a framework grounded in systemic functional linguistics (SFL). The analysis also considers how humans process cognitive-linguistic features when rendering poetic language. Results indicate that ChatGPT outperformed both MT systems and human translators in structural and semantic coherence, as well as in preserving poetic features such as rhyme and mood. However, all automated systems struggled with context-rich dimensions like tenor and mode, underscoring the enduring value of human interpretive depth. The findings highlight the potential of AI language models to complement, rather than replace, human expertise in literary translation and advocate for hybrid approaches that integrate computational efficiency with poetic language and cultural sensitivity. Acknowledgements We would like to express our sincere appreciation to the human translators who contributed to this study by translating the selected poem from Arabic into English. We equally thank the two translation experts who contributed to the analysis of translations. Their careful attention to linguistic nuance and cultural context played a significant role in comparing the machine-generated and human-produced translations. The authors are thankful to the Deanship of Graduate Studies and Scientific Research at the University of Bisha for supporting this work through the Fast-Track Research Support Program. Disclosure Statement The authors reported no potential conflict of interests.

Purpose This study intends to unfold the potential of multimodal pedagogy for fostering inclusivity and engagement in English language learning in higher education. Design/methodology/approach Using a mixed-method approach, data were collected through classroom observations, teacher interviews and social media analysis. Findings The findings indicate that multimodality provides more opportunities for structured, inclusive and engaging teaching. Despite efforts to enhance multimodality in the three contexts, much of the learners’ multimodal activities remain a topic of contention, raising questions about measuring learners’ multimodal outcomes. Research limitations/implications The main caveat stems from the small sample size, which restricts the generalizability of the findings. Practical implications The study proposes implications for advancing multimodal practices in teaching and assessing the English language across contexts. It proposes practical tools such as grading contracts, digital badges and adaptable assessment frameworks that promote inclusivity and engagement while allowing for flexible evaluation of students’ multimodal competencies. Originality/value While inclusivity is recognized as a central pillar in education reform and development, linking it to multimodal pedagogy is a gap that leaves an important opportunity for the present endeavor to advance research in this area.

AI systems generate educational content, yet their rhetorical characteristics remain insufficiently understood. This study examines epistemic stance construction through hedging and boosting devices in AI-generated discourse based on one hundred fifty AI-generated texts sampled from educational, professional, and conversational domains. Using a systematic corpus-based mixed-methods analysis that combined quantitative frequency measurements with qualitative functional interpretation, this investigation reveals significant asymmetry in rhetorical positioning. Educational texts displayed a mean hedging frequency substantially higher than booster deployment, creating a hedge-to-booster ratio that far exceeds patterns documented in human pedagogical discourse. Qualitative analysis identified numerous instances of inappropriate hedging in foundational content where confident presentation would better support learning, alongside relatively sparse and structurally formulaic use of boosters. These findings suggest that AI systems overgeneralize cautious patterns learned from training data to contexts that call for assertive instructional guidance. They also show critical limitations in current AI-based tools and establish frameworks for developing AI-mediated learning that supports rather than undermines knowledge construction in digital communities.

ObjectiveTo examine the association between 24-hour movement behaviors and depressive symptoms in older adults using compositional data analysis, and to investigate the dose-response characteristics of time reallocations between movement behaviors in relation to depressive symptoms.MethodsA cross-sectional study was conducted among 1093 urban-dwelling older adults aged 60 years and above in selected communities of Jinan City, Shandong Province, China, between April 2024 and September 2024. The Chinese version of the International Physical Activity Questionnaire-Long Form (IPAQ-LF) was used to estimate time spent in moderate to vigorous-intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SB), and sleep (SLP) across a typical 24-hour day. The Chinese version of the Patient Health Questionnaire Depression Scale-9 item (PHQ-9) was applied to assess depressive symptoms. Compositional isotemporal substitution models were employed to explore the associations between time reallocations among 24-hour movement behaviors and depressive symptoms, accounting for the co-dependent nature of time-use data.Results(1) The geometric means of time spent in MVPA, LPA, SB, and SLP were 25.33 minutes, 141.26 minutes, 738.10 minutes, and 455.15 minutes, respectively. Variation matrix analysis revealed the highest log-ratio variance between MVPA and SB (0.168), and the lowest between SLP and SB (0.031). (2) The prevalence of screening-positive depressive symptoms was 16.29% among Chinese urban older adults. (3) Results from compositional linear regression models showed that time allocated to MVPA, LPA, and SLP (relative to the remaining movement behaviors) was negatively associated with depressive symptoms, while time spent in SB was positively associated. (4) Dose-response analysis further indicated that: (a) MVPA substitutions with other movement behaviors exhibited nonlinear and markedly asymmetric effects on depressive symptoms; (b) replacing MVPA with LPA, SB, or SLP resulted in increasingly larger changes in predicted scores as substitution duration increased, whereas the reverse substitution (MVPA for other movement behaviors) produced progressively smaller changes; and (c) substitutions between SB and LPA displayed linear and symmetrical effects.ConclusionsThe findings provide evidence of an association between 24-hour movement behaviors and depressive symptoms in Chinese urban-dwelling older adults and reinforce the importance of achieving a balance between different types of movement behaviors over a 24-hour period for mental health.