This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis.

The research investigates technical talent market segmentation through a comparison between standard recruitment methods from public job listings and the premium senior engineering recruitment model of Talent by Blind. The research uses organisational ambidexterity theory to explain how businesses achieve efficient mainstream hiring (exploitation) and innovative recruitment approaches (exploration) for their senior engineering positions. The research analyses 7,927 LinkedIn job postings to identify the main specialisations and seniority levels and work arrangements found in the accessible technical workforce. The research shows that most job postings on public job boards focus on hiring mid-level professionals working in Indian technology centres, yet director and executive positions make up only 1% of available positions. The premium senior engineering segment uses private recruitment networks and candidate acquisition methods instead of traditional job posting strategies. The research demonstrates that organisations use ambidextrous recruitment approaches to handle talent shortages and technological changes and changing workforce needs through their different recruitment systems for high-end and standard positions. The research combines market segmentation methods with organisational ambidexterity theory to explain how organisations use different recruitment approaches for modern technology workers.

The public sector maintains official salary systems and equality policies yet women and men continue to experience pay differences when total compensation is evaluated. The research conducts a thorough examination of gender pay equity throughout a large municipal workforce by studying both base salary and overtime and longevity pay. The research uses administrative data from more than 10,000 employees spread across 42 departments to analyze compensation disparities through a theoretical framework that includes Human Capital Theory and Discrimination Theories and Institutional Theory and Gender Stratification. The research shows that unequal overtime distribution creates more than 60% of the total pay difference between genders. The departmental level shows substantial differences because cultural elements together with structural aspects determine how equity outcomes develop. The departments that demonstrate strong equity performance provide practical examples which organizations can use to develop reform strategies. The research demonstrates that meaningful gender pay equity in public sector organizations requires specific data-based interventions together with enhanced organizational responsibility. Note: This analysis is cross-sectional in design; therefore, the findings reflect associations rather than causal relationships, and longitudinal replication is recommended to establish causality.

This study investigates the linguistic, structural, and behavioral characteristics of "brain rot" content on TikTok, an emerging phenomenon defined by cognitively shallow, hyper-stimulating videos optimized for maximum passive engagement. The study aims to operationalize and detect "brain rot" content and to examine how such content relates to user engagement and interaction quality. Drawing on a large-scale dataset of 78,452 TikTok videos, we employed a mixed-methods design combining Natural Language Processing, regression modeling, and qualitative coding. This approach enabled the identification of key textual, structural, and behavioral markers associated with low-cognitive-value media content. The findings show that videos under 15 seconds, clickbait language, high transition frequency, and emotionally triggering content are significantly associated with higher view counts but lower comment depth. The results also indicate that verified and unverified creators contribute to the diffusion of "brain rot" content across different topic areas, suggesting that platform architecture shapes both content visibility and content quality. This study contributes a novel operational framework for identifying "brain rot" content and advances theoretical understanding of attention economics and algorithmic cultivation in short-form video environments. The findings highlight the broader implications of digital content strategies for user cognition, media literacy, and platform governance.

This study examines how personality traits shape leadership communication on social media outlets by comparing the Twitter/X/X discourse of Donald Trump and Elon Musk. Employing a mixed-methods content analysis of 2,391 original tweets collected between December 2021 to December 2023, natural language processing techniques are integrated—lexical diversity measures, part-of-speech distributions, sentiment analysis, topic modeling—and both closed- and open-vocabulary approaches for Big Five trait inference. Also conduct time-series and crisis-specific analyses to capture temporal evolution and response strategies. Analysis of normalized engagement metrics reveals that Trump achieves superior per-follower interaction rates, generating 1,101 retweets and 4,119 likes per million followers compared to Musk's 494 retweets and 2,201 likes per million followers, indicating distinct audience engagement strategies optimized for different communication objectives. By bridging trait-based leadership theories with computational linguistics, this research demonstrates that social media platforms facilitate diverse, personality-congruent leadership expressions rather than enforcing uniformity. Practically, findings suggest that effective digital leadership requires aligning social media strategies with authentic identity and contextual objectives. Crisis communication should extend established patterns rather than relying on generic templates.

This study investigates how legal chatbots are framed, contested, and evaluated in online public discourse, with a focus on trust, ethics, and regulatory expectations. Analyzing 2514 texts published on The Guardian and X (Twitter) between 2022 and 2024, the study integrates BERTopic-based topic modeling, VADER sentiment analysis, and qualitative thematic coding to capture both large-scale patterns and contextual nuance. The findings show that public perceptions of legal chatbots are not uniform but strongly shaped by application domain. Chatbots used in child protection and technology-driven services are predominantly framed as supportive and empowering tools, generating relatively higher levels of expressed trust. By contrast, deployments in government and social justice contexts are associated with skepticism, reflecting concerns about surveillance, misinformation, and the reinforcement of institutional bias. The study contributes to Algorithmic Justice scholarship by demonstrating that fairness perceptions emerge from institutional power relations rather than technical features alone, advances Digital Trust Theory by foregrounding affective dimensions of trust alongside cognitive evaluations, and introduces regulatory asymmetry as a diagnostic lens for identifying systematic gaps between ethical concern and regulatory oversight as expressed in public discourse. Together, these findings highlight the limits of one-size-fits-all AI governance and point to the necessity of context-sensitive regulatory and design approaches for AI-powered legal technologies.