This study explores how mother tongue-based multilingual education, strengthened with appropriate educational technology, can advance social and educational empowerment among minority ethnic communities in Bangladesh. It draws on sociolinguistics and the sociology of education to treat language as more than a classroom tool. Language is also a pathway to identity, dignity, participation, and equitable life chances. The research asks how technology-supported instruction affects engagement, attendance, learning achievement, and progression, and how it shapes language attitudes, classroom interaction, and community involvement. A mixed methods design is proposed. Linguistic ethnography will document home and school language practices through classroom observation, interactional transcripts, and interviews with students, parents, teachers, and local leaders. Quantitative measures will track baseline and endline learning outcomes, retention, and teacher practice indicators. The project co-designs and evaluates a low-cost digital support package that works in low-connectivity settings, including offline-first micro-lessons, community-recorded audio stories, bilingual e-books aligned with curriculum goals, and teacher learning modules on inclusive pedagogy and translanguaging. It also examines digital constraints such as device sharing, connectivity gaps, and varied literacy levels so that technology reduces rather than reproduces inequality. The expected contribution is practical evidence for language in education policy, scalable edtech design, and culturally sustaining teaching that can reduce dropout risk, improve foundational learning, and strengthen social inclusion through mother tongue education.

Environmental degradation in Indonesia often affects foothill communities most severely. Deforestation, land conversion, and extractive activities increase the risks of floods, landslides, and the gradual loss of local livelihoods. In Aceh, these problems continue despite the presence of environmental regulations. Weak enforcement and limited monitoring allow activities such as illegal logging and land encroachment to persist. As a result, environmental damage is not only an ecological issue but also a matter of justice, since vulnerable communities bear the consequences of violations that are rarely addressed through effective policing or legal action.

This paper examines forest waqf as a community-based response to these challenges. Waqf is an Islamic endowment in which property or assets are permanently dedicated for public benefit. Once designated as waqf, the asset cannot be sold or transferred and must be used for social or charitable purposes. When applied to forest land, waqf creates a long-term protection mechanism that prevents commercial exploitation and preserves environmental resources for the wider community. The study uses a qualitative case study of forest waqf initiatives in Aceh, drawing on document analysis.

The findings show that forest waqf functions not only as a conservation effort but also as a form of local environmental governance. By placing forest areas under perpetual waqf ownership, communities limit activities such as illegal logging and uncontrolled land conversion, which often occur beyond the reach of formal monitoring. For communities living near forested slopes, these initiatives have practical benefits. Protected forests help maintain water sources, stabilize land, and reduce exposure to environmental hazards. In this sense, forest waqf contributes to environmental protection while also addressing the social impacts of environmental harm. The study argues that forest waqf represents a preventive approach to environmental justice. Instead of relying mainly on policing and punishment, it focuses on long-term stewardship, collective responsibility, and ethical land management.

Algorithms and artificial intelligence (AI) are often portrayed as powerful tools intended to enhance performance and optimize tasks. Yet critical scholarship stresses that these systems operate as socio-technical actors: they are conceived, trained, and deployed within particular institutional and social arrangements, and their outputs may generate distortions in the very domains they are meant to improve. Spatial discrimination—an enduring phenomenon—emerges when resources, opportunities, or services are allocated in ways that sustain or intensify geographic inequalities, sometimes through apparently neutral criteria that nonetheless disadvantage specific places or populations. This study offers a systematic literature review that examines how AI may interact with territorial dynamics and how spatial inequalities can be reproduced through algorithmic decision-making. It identifies two key dimensions through which territorial discrimination can take shape, helping to frame where discriminatory effects may originate and how they may be experienced across space. This paper argues that a critical approach to algorithm implementation is essential to ensure ethical, transparent, and rigorous assessment. Such an approach requires careful scrutiny of underlying assumptions, data sources, proxies for location, and decision rules, as well as attention to governance and accountability in real-world deployments. Overall, this study underscores that AI is not merely a technical instrument but also a force that can reshape spatial governance and influence whether geographical inequality is reinforced or reduced over time.

Abstract

The phenomenon of self-diagnosis regarding mental health disorders has escalated significantly due to the massive influx of educational content aggressively personalized by social media algorithms. The novelty of this study lies in identifying the phenomenon of 'digital identity fusion,' where social media algorithms do more than validate symptoms; they reconstruct mental health labels into instruments for social navigation and self-aesthetics that paradoxically diminish the user’s self-efficacy. This study aims to elucidate how algorithmic mechanisms contribute to reshaping the perception of mental illness, shifting the paradigm from a clinical diagnosis to a central facet of the user’s self-identity. Methods: Employing a qualitative phenomenological approach (Interpretative Phenomenological Analysis), this research involved three participants (n = 3) selected through purposive sampling to represent different social pressure spectrums: a university student, a private employee, and a creative freelancer. Primary data were collected through in-depth interviews focusing on the history of algorithmic exposure and the motivations behind self-diagnostic behavior. Results: The results indicate that algorithms create echo chambers that trigger confirmation bias, leading individuals to adopt labels such as ADHD or Anxiety as self-defense mechanisms against external pressures. The findings reveal an identity fusion in which mental health labels are used as aesthetics or personal branding to construct a unique or "authentic" self-narrative within digital spaces. Collectively, these interactions with algorithms have diminished participants' self-efficacy, as individuals feel bound to a permanent "disease identity". Conclusion: The study concludes that self-diagnosis has shifted from a mere search for medical information toward the formation of a core identity used for social navigation. Consequently, future psychological interventions must integrate algorithmic literacy to restore individual personal agency.

The global push toward e-government has raised critical questions about whether digital transformation is equally accessible across demographic groups. While digital literacy has been widely recognised as a key determinant of e-government adoption, the moderating role of gender in this relationship remains insufficiently explored, particularly in sub-Saharan Africa where structural inequalities intersect with digital access disparities. This study investigates whether sex significantly moderates the relationship between digital literacy and e-government adoption among Nigerian citizens. A quantitative cross-sectional survey design was employed, with structured questionnaires administered to 369 respondents selected through stratified random sampling in Ilorin, Kwara State, Nigeria. Digital literacy, e-government adoption, and demographic variables were measured using validated multi-item scales. Data were analysed using Structural Equation Modelling (SEM) via SmartPLS, with multi-group analysis (MGA) applied to test the moderating effect of sex across male and female subgroups. Findings confirm that digital literacy exerts a significant positive influence on e-government adoption. However, multi-group analysis reveals that sex significantly moderates this relationship, with digital literacy demonstrating a stronger predictive effect on e-government adoption among male respondents than female respondents, reflecting persistent gendered digital inequalities in the Nigerian context. The study concludes that e-government strategies that overlook gender as a structural variable risk deepening existing digital divides. Policymakers and public administrators must adopt gender-sensitive digital literacy interventions to ensure equitable access to and benefits from e-government platforms in developing countries.

Introduction:
The accelerating digital transformation of the tourism sector, driven by the expansion of digital platforms, data management systems, and artificial intelligence applications, is reshaping labor relations and skill requirements. In this context, the development of leadership, social/communicative, and digital skills has become critical to the sustainability and competitiveness of tourism enterprises. This study examines the role of these skills within the tourism and cultural sectors in Crete, with particular emphasis on the gender dimension.

Methods:
This study is based on quantitative research employing structured questionnaires (including both closed- and open-ended questions) distributed to more than 500 tourism and cultural enterprises across all regional units of Crete. The final sample consisted of 287 enterprises. The analysis focused on employers’ and senior executives’ perceptions of essential leadership competencies, employees’ social/communicative and digital skills, and organizational capacity for adaptation to technological change.

Results:
The findings indicate that effective leadership, combined with the enhancement of digital and communicative skills, constitutes a key factor in strengthening organizational resilience. At the same time, the transition toward increasingly digitalized work environments appears to affect professional opportunities for men and women in distinct ways, potentially reinforcing existing gender disparities.

Conclusions:
Sustainable development in the tourism sector requires empowering leadership models and inclusive strategies for digital skills development. Integrating a gender-sensitive perspective into training policies and organizational development initiatives is essential to prevent the reproduction of inequalities within the broader context of technological transformation.

Growing reliance on digital infrastructures has intensified exposure to cyber threats, yet such risks are not evenly distributed across social groups. Drawing on feminist criminology and digital safety scholarship, this study examines gendered patterns of cyber (in)security in Türkiye by focusing on four dimensions: (1) cyber risk perception, (2) protective behaviors (“cyber hygiene”), (3) cyber-technical literacy, and (4) cyber victimization. The analysis distinguishes harassment-based harms (e.g., cyberbullying, leaking, stalking) from financially motivated attacks (e.g., phishing, ransomware, fraud), in line with research documenting gendered threat landscapes online. Data were collected via an online survey (N=387) administered to adult internet users in Türkiye. Key measures included a cyber risk perception index (endişe_), protective behavior index (onlem_), cyber-technical literacy index (bilgi_*), and a victimization score (scvic). Independent-samples t-tests and MANOVA were applied to assess gender effects. Results revealed significant multivariate differences (Wilks’ Λ=.916, p<.001). Univariate tests indicated that women scored higher on risk perception (p=.030) and slightly higher on protective behaviors, while men reported higher cyber-technical literacy (p=.061, marginal) and significantly higher victimization (p=.018). These asymmetries illustrate how gendered socialization and differential threat exposure shape online safety practices. Findings demonstrate that cyber insecurities are not merely technical phenomena but are embedded in broader socio-cultural structures.

In the era of machine learning-driven predictive systems, data anonymization and perturbation techniques have become widespread for protecting individual and group privacy and simulating rare events. Yet these approaches frequently undermine causal inference, reduce model accuracy, and enlarge predictive gaps—the critical disparity between statistical inferences derived from training data and definitive operational predictions.

This review investigates the inverse relationship between privacy preservation and predictive performance, conceptualizing it as a contemporary value conflict embedded in technicism: over-reliance on technical fixes that unintentionally intensify systematic biases within intelligent automation.

Central research questions include the following: Does privacy protection via data perturbation obstruct models’ direct grasp of human behavioral patterns? What are the downstream effects of predictive gaps on task automation and societal biases? Drawing heavily on Max Weber’s value pluralism, instrumental rationality, and the (iron cage) metaphor—alongside Isaiah Berlin’s insights—the analysis demonstrates how technocratic solutions and unilateral privacy advocacy fail to resolve the inherent tension, reducing a deep value clash to mere technical problems and perpetuating a vicious cycle of increasing complexity.

A hybrid methodology combined horizontal and vertical literature reviews. From Scopus, IEEE, and Google Scholar (2019–2025), 21 sources were selected using keywords “Machine Learning,” “Model Accuracy,” and “Privacy,” then coded in MAXQDA 24 across five categories, revealing four major gaps: insufficient social-science framing, the need for multidimensional assessment, technical–social disconnect, and unexamined sociological privacy–accuracy links.

Findings show that “infrastructural privacy” widens predictive gaps, echoing Weber’s iron cage where rationality traps rather than liberates. Real-world cases (supermarket pregnancy targeting, counterterrorism metadata analysis) highlight privacy violations and error amplification via proxy data. While protective, synthetic data and differential privacy distort genuine causal relations, fostering biases in sensitive automation domains such as policy-making.

The review concludes that meaningful resolution demands explicit value-based trade-offs_neither absolute technicism nor uncompromising privacy absolutism_to curb systematic biases in socio-technical systems.

As media production and distribution increasingly integrate recommender systems, generative AI, and conversational interfaces, gatekeeping shifts from a primarily editorial function to a distributed, continuously optimizing socio-technical process. Building on recent work on AI-enabled gatekeeping, automated content pipelines, and platform governance, this paper advances a conceptual model of AI gatekeeping across media production and couples it with an RL-informed agent perspective to clarify where—and how—societal values are operationalized (or displaced) by optimization objectives. The conceptual model decomposes media gatekeeping into an end-to-end pipeline of decision points that apply beyond journalism to creator economies and platform-native formats: (1) creator and source inclusion (who gets visibility and monetization access), (2) topic/format selection and trend detection, (3) content generation and editing (text, audio, image, video), (4) packaging and metadata (titles, thumbnails, tags, captions), (5) ranking/recommendation and feed placement, (6) moderation and safety filtering, and (7) distribution feedback loops (engagement, retention, and revenue signals). Each stage is specified in terms of actors (creators, studios, newsrooms, platforms, vendors), artifacts (training data, prompts, style guides, policies), and outcomes (visibility, framing, attention allocation, and cultural salience). The RL-informed perspective treats AI-mediated media gatekeeping as sequential decision-making under constraints, where agents learn policies that trade off timeliness, quality, novelty, audience satisfaction, and harm minimization. This framing makes explicit a core societal risk: when reward signals are proxied by engagement and watch-time, systems can rationally learn behaviors that amplify outrage, optimize for addictive consumption patterns, privilege already-dominant aesthetics, or systematically under-expose minority creators and niche cultural topics—often without any single decision appearing normatively problematic in isolation. The paper proposes evaluation dimensions aligned with social-science concerns—exposure equity, plurality of cultural representation, provenance and disclosure, and user contestability—paired with RL-relevant diagnostics (reward specification audits, off-policy evaluation, counterfactual exposure tests, and drift monitoring).

Domestic violence is usually viewed as a crime and a civil cause of action committed against a woman, but men are also victims of violence in the domestic and marriage relations. Violence against men, physical, psychological, emotional and economic is a subject that has been largely ignored in Indian society. The current law system in India is mainly aimed for the protection of women in the form of legislations as the Protection of Women from Domestic Violence Act, 2005 and the Bhartiya Nyaya Sanhita, 2023 that have laid out the remedies to the female victims. As a result, male victims of domestic violence are usually impeded in accessing justice and protection by legal, social, and psychological means. Many fundamental rights in the Constitution of India include equality before the law, protection of life and personal liberty, and the right to life with dignity. Such constitutional guarantees are supposed to be offered equally to all persons irrespective of gender. Nevertheless, the lack of gender-neutral legislation on domestic violence is a cause of the concern about the safeguarding of human rights of men in India. This study critically focuses the legal context of domestic violence in India and determine whether it provides sufficient protection of the human rights of male victims. The research also emphasizes the social stigma, culture, and institutional issues that deny men the chance to report domestic abuse. The methodology of the research is the doctrinal and analytical approach. The research provides the legal challenges and human rights considerations of domestic violence against men and contributes in advancing the current and new perspective of gender-neutral laws.