Doctoral education in library and information science (LIS) is a workforce-development mechanism through which the profession builds research capacity, academic leadership, and evidence-informed institutional practice. Yet little is known about how structural opportunity, professional capital, and institutional access conditions shape expressed interest in doctoral LIS study within national librarian populations. This article develops an interpretable predictive segmentation framework using the Philippine Librarians Census to classify expressed doctoral study interest and translate empirically derived workforce segments into ethically bounded recruitment personas. The study uses a cross-sectional, sequential predictive segmentation design combining descriptive workforce profiling, interpretable machine-learning classification, calibration and subgroup diagnostics, segmentation analysis, and persona translation safeguards. The findings show that expressed doctoral study interest is patterned across educational, professional, institutional, and geographic contexts, supporting the use of predictive analytics as a planning tool rather than as an admissions, ranking, or individual forecasting system. The article contributes to LIS workforce research by demonstrating how national professional census data can support doctoral pipeline planning while preserving inferential restraint, interpretability, fairness awareness, and non-exclusionary governance.

Election monitoring increasingly depends on the capacity to process high-volume citizen reports, social media posts, and platform-based submissions under conditions of uncertainty. This study evaluates whether a zero-shot large language model (LLM) pipeline can function as a high-recall triage system for election monitoring using VoteReportPH data from the 2025 Philippine elections. Drawing on signal detection theory, information overload theory, and human-AI complementarity, the study frames LLM classification as decision support rather than autonomous adjudication. The analysis used a postprocessed Election Monitoring System dataset of 3,618 reports and a cleaned model-evaluation dataset of 4,158 reports. For binary validity detection, the model correctly surfaced 166 of 181 human-validated reports, yielding a recall of 0.9171, specificity of 0.8973, and accuracy of 0.8983, but low precision of 0.3198. This error profile indicates a recall-oriented filter that reduces missed incidents while forwarding false positives for human review. In multiclass incident categorization, the model performed strongly on explicit categories such as automated counting machine errors and illegal campaigning, but weakly on rare, residual, and procedurally ambiguous categories. The findings show that zero-shot LLMs can support civic monitoring as triage infrastructure, but they require human verification, transparent error handling, and category-specific workflow design.

This paper proposes ginhawa—a culturally grounded condition of breath, ease, and shared presence—as a more accurate framework for understanding Filipino well-being than Western models centered on individual self-actualization. Drawing from Sikolohiyang Pilipino, postcolonial critiques, and ethnographic accounts of travel practices, the study examines how Filipino tourists pursue relief from sikip (tightness) through embodied and relational forms of rest. Travel becomes meaningful when it restores physiological vitality, renews social ties, and redistributes comfort across kinship and community networks. Analyses of balikbayan returns, pasalubong exchanges, food rituals, and suki relationships show that Filipino mobility prioritizes reconnection, collective enjoyment, and the circulation of ginhawa rather than novelty or solitary exploration. The paper argues that tourism governance and marketing strategies grounded in Western assumptions obscure these cultural orientations. A decolonial reframing positions tourism as part of the right to ginhawa—an ethical commitment to creating conditions that allow all Filipinos to breathe with ease.

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.