Public libraries function as civic and social infrastructure whose value depends partly on residents’ ability to physically reach them. This study evaluates whether the spatial allocation of Quezon City Public Library (QCPL) branches aligns with barangay-level population demand under pedestrian-network constraints. Rather than treating branch presence or straight-line proximity as sufficient evidence of access, the study conceptualizes public-library equity as the distribution of potential service exposure produced by the interaction of branch supply, population concentration, and network impedance. Barangay-level pedestrian accessibility is measured using shortest network distance to the nearest QCPL branch, cumulative distance thresholds, population-weighted coverage, geodesic-network comparison, district disparity analysis, and spatial clustering diagnostics. Accessibility burden is operationalized through population-distance exposure and evaluated using Moran’s I, Local Moran’s I (LISA), and Getis-Ord Gi* statistics. Results indicate substantial inequities in pedestrian accessibility. Only 36 of 142 barangays, representing 452,646 residents or 14.7% of the city population, fall within an 800 m network-distance catchment. Coverage increases to 49.8% of the population within 1,600 m and 73.7% within 2,400 m, leaving 812,484 residents beyond the 2,400 m threshold. Mean network distance (1,441.3 m) substantially exceeds mean geodesic distance (918.2 m), and 87.3% of barangays exhibit network distances more than 25% longer than their geodesic equivalents, demonstrating that straight-line proximity systematically overestimates practical pedestrian access. Spatial autocorrelation analysis reveals statistically significant clustering of accessibility burden, with high-burden hot spots concentrated in northern Quezon City, particularly Districts 2, 5, and 6. The findings demonstrate that branch counts and circular buffers alone are insufficient indicators of equitable public-library provision. The study contributes a network-based, population-weighted, and spatially explicit framework for evaluating urban public-service accessibility and offers an applied methodology for infrastructure-equity planning in Global South cities.

Automated misinformation verification has increasingly shifted from surface-text classification toward evidence-grounded claim verification. While large language models (LLMs) demonstrate strong language understanding capabilities, standalone configurations remain vulnerable to unsupported factual judgments and hallucination. Retrieval-augmented generation (RAG) has emerged as a potential mechanism for improving evidence grounding by supplying external information during inference, yet empirical findings remain inconsistent regarding when retrieval improves or redistributes classification reliability. This study evaluates retrieval augmentation as an empirical verification condition rather than as an assumed solution. Using a quantitative comparative benchmark design, the study compares three verification architecture types on short fact-checked English claims: (1) a TF-IDF + Logistic Regression baseline, (2) standalone LLM configurations, and (3) bounded-corpus RAG LLM configurations. The benchmark evaluates one traditional baseline and five LLM families under controlled retrieval conditions using a binary misleading/not-misleading classification task derived from the LIAR dataset. Performance is evaluated using Macro F1, class-level recall, precision, Matthews Correlation Coefficient (MCC), confusion matrices, statistical significance testing, error agreement analysis, textual pattern analysis, and subject-level performance analysis. The study additionally examines ambiguity-sensitive claim conditions, including numerical ambiguity, temporal ambiguity, and partial-truth structures, to identify claim categories that remain difficult across architectures. The study positions retrieval augmentation as a retrieval-conditioned evidence-grounding mechanism whose effects depend on evidence relevance and contextual alignment rather than as an inherently superior verification approach. Findings contribute to misinformation verification research, evidence-grounded AI evaluation, and Library and Information Science discussions concerning retrieval, credibility-related classification reliability, and responsible AI-assisted verification. The study further demonstrates the importance of balanced reliability metrics and ambiguity-aware evaluation in benchmark-based misinformation research.

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.