This paper examines how ginhawa—a culturally rooted Filipino concept of breath, ease, and shared well-being—offers an alternative to dominant Western models of self-fulfillment in tourism. While global tourism discourse elevates individualism, self-actualization, and the solitary quest for personal growth, Filipino experiences of travel reveal a fundamentally relational orientation. Drawing from Sikolohiyang Pilipino, critical well-being studies, and decolonial scholarship, the paper argues that Filipino travelers seek not autonomy but restored equilibrium, embodied relief, and renewed connection. Through analyses of pahinga (rest), pakikipagkapwa (relational belonging), gana (appetite), and social practices such as balikbayan homecomings, pasalubong, and suki relations, the study shows how tourism functions as a means of recovering vitality and circulating comfort across kinship networks. The paper concludes by outlining policy implications for a decolonized tourism industry that recognizes rest as a right, foregrounds communal needs, and supports culturally grounded practices of well-being.

In recent years, tourism has become increasingly “datafied”—with large volumes of information generated via mobile devices, social media, booking systems, and location sensors. While big data promises improved insights for planning, marketing, and managing tourist flows, it also raises deep questions about power, equity, and ethics. This paper examines the use of big data in tourism through the lens of Data Justice and Critical Data Studies, focusing on how data practices can reinforce or challenge inequalities among different stakeholders (locals, tourists, government, private platforms). Centering on four dimensions of justice—distributive, recognitional, representational, and procedural—the study theorizes how decisions about who collects data, how it is used, and for whose benefit, affect communities and destination governance. Through illustrative cases and theoretical exploration, the paper argues that a just tourism data regime must embed transparency, participation, accountability, and redress mechanisms. Ultimately, it proposes a framework for more equitable and ethical use of big data in tourism, aiming to guide policymakers, destination managers, and communities toward fairer data practices.

This study assesses whether media and information literacy (MIL) coursework enhances undergraduates’ abilities to detect fake news and influences their sharing behavior online. Sixty-six Filipino students—comprising those who completed LIS 50, those in LIS 10, and those with no MIL training—each evaluated 28 Facebook-style headlines (14 real, 14 fake) for accuracy and indicated whether they would share them. Accuracy and sharing scores (0–28) were compared across groups using one-way ANOVAs. No significant effects emerged: accuracy scores were nearly identical for MIL (M = 9.91, SD = 1.63) and No MIL (M = 9.90, SD = 1.92; F(1,60)=0.0006, p=0.989), and sharing intentions did not differ (MIL: M = 2.59, SD = 2.33; No MIL: M = 2.43, SD = 2.70; F(1,60)=0.063, p=0.803). Thematic analysis of open-ended responses revealed that source credibility, linguistic cues, and verification practices guide accuracy judgments, while ethical responsibility, audience relevance, and emotional engagement drive sharing decisions. Findings suggest that a single MIL course may be insufficient to produce measurable improvements in fake-news discernment or change sharing behavior. We recommend integrating MIL across curricula, employing scenario-based simulations, implementing reflective sharing exercises, and conducting longitudinal assessments. All materials and code are available at https://github.com/panda-lab-slis/informationliteracy.

Artificial intelligence (AI) is rapidly becoming integral to research and learning in higher education. Academic librarians, as information literacy experts, are uniquely positioned to help students develop **AI literacy** – the knowledge and skills to understand and use AI effectively and ethically. The Digital Education Council (DEC) @digitaleducationcouncilDigitalEducation AI Literacy Framework defines five key dimensions of AI literacy for all learners. This guide is organized around those five dimensions, illustrating the role librarians can play in each, along with practical teaching strategies, tools, and activities. It concludes with recommendations for a student-facing AI Literacy Workbook aligned to these dimensions. The focus is on actionable approaches in a higher education context, grounded in current best practices in academic libraries.

In an era of rapid digital transformation and expanding knowledge infrastructures, the pursuit of doctoral education by librarians is vital for fostering innovation, leadership, and equity in the library and information science (LIS) profession. However, the factors influencing librarians’ intent to undertake doctoral studies remain underexplored, particularly in developing contexts such as the Philippines. This study leverages the nationwide Philippine Librarians Census dataset and employs supervised machine learning techniques (logistic regression and random forest models) to predict which librarians are most likely to aspire to Ph.D. study within five years. Results reveal that ongoing academic engagement (current enrollment in further studies), professional role, years of service, and institutional type are among the strongest predictors of doctoral intent. Model performance was moderate (AUC equals 0.69), with both models highlighting similar key features. The findings underscore persistent disparities related to sector and region, suggesting that targeted support and policy interventions are needed to ensure equitable access to advanced LIS education. This study demonstrates the value of data-driven approaches for workforce planning and contributes new empirical evidence to guide educational and institutional strategies in digital library environments.

This paper investigates how structured training in information evaluation shapes the cognitive and attitudinal defenses that individuals deploy when encountering potential misinformation online. We examine two constructs—conflict detection, defined as the skill to recognize contradictions in information, and open-mindedness, the willingness to consider opposing viewpoints—across two student populations: those enrolled in Library and Information Science (LIS) programs and their non-LIS peers. By administering a cross-sectional survey that measures performance on the Cognitive Reflection Test, scores on the Actively Open-minded Thinking scale, and scenario-based sharing intentions, we test whether formal training amplifies the protective effects of these traits. Using hierarchical regression and interaction analyses, we evaluate differences in main effects and moderation patterns between the groups. Results will clarify how dual-process theories and meta-cognitive attitudes interact to curb misinformation sharing and will inform the design of targeted educational interventions that bolster digital literacy.