RAIS Journal for Social Sciences

The AI Conceptual Learning Framework (AICLF): A Model for AI Learning Laboratories in Education and Organizations

2026-04-20T14:30:16+00:00

This study introduces the AI Conceptual Learning Framework (AICLF), a conceptual model designed to support the development of artificial intelligence understanding through structured reasoning, sociotechnical awareness, and adaptive learning processes. The framework addresses limitations in traditional instructional approaches that often emphasize procedural skill acquisition over conceptual comprehension. The AICLF is organized around interconnected conceptual dimensions that collectively support learner engagement with AI-enabled environments, fostering interpretation, evaluation, and decision-making within complex contexts. By integrating principles from artificial intelligence education, adult learning, and sociotechnical systems, the framework provides a foundation for advancing conceptual understanding and applied reasoning. This study contributes a theoretically grounded model that may inform instructional design, workforce development strategies, and AI literacy initiatives across diverse educational and professional settings.

Active Listening, Storytelling, and the Employee Experience: A Phenomenological Study of Leadership Communication, Stress, and Work-Life Balance

2026-04-20T14:35:50+00:00

This phenomenological study explores how employees experience leadership communication, focusing on active listening and storytelling as relational practices that influence work-life balance, stress, and the employee experience. Guided by Moustakas’s qualitative framework, data were collected through a two-step process consisting of a written reflection questionnaire, followed by semi-structured interviews, to capture participants' lived experiences. Data were analyzed using iterative coding procedures to identify meaning units and develop thematic clusters. Findings show that active listening provides validation, clarity, and emotional support, while storytelling aids sensemaking and strengthens shared purpose. Together, these behaviors shape employees’ well-being, boundary management, and commitment. The study contributes new insight into how communication functions as a psychological and relational resource in contemporary workplaces.

Designing an AI Governance Program: A Control-Based Model for Risk and Compliance

2026-04-20T14:51:08+00:00

As organizations increasingly use artificial intelligence (AI) for organizational decision-making, cybersecurity, and compliance, the limits of principle-based AI governance have become clear. Frameworks have revealed limitations in principle-oriented AI governance approaches. While frameworks such as the NIST AI Risk Management Framework provide broad, high-level guidance, many organizations still lack practical, auditable mechanisms to operationalize AI governance within their enterprise governance, risk, and compliance (GRC) programs. This research introduces a control-based AI governance model that embeds AI oversight into existing internal controls and risk management structures. The model organizes governance through administrative, technical, and operational controls, including integrating AI risk assessment, compliance mapping, and continuous monitoring throughout the AI lifecycle. Governance controls are mapped to the NIST AI Risk Management Framework and NIST Special Publication 800-53 to demonstrate compatibility and traceability, without requiring demonstration of operational compatibility or mandating specific technologies. This study presents a practical, technology-neutral approach to help organizations implement AI governance and align it with their GRC efforts, promoting an agnostic approach that advances the operationalization of AI governance and its integration with enterprise GRC practices.

Assessing the Internal Worker Upward Mobility Paradox from the Voices of Women of Color

2026-04-20T15:02:20+00:00

Internal upward mobility is frequently framed as a strategic mechanism for leadership continuity and workforce sustainability, yet many organizations fail to capitalize on the talent they have already developed. This qualitative study examines the consequences and missed opportunities that arise when organizations neglect internal employee talent and rely on conventional or externally focused talent management practices. Centering the lived experiences of twelve women of color, African American, Arab American, and Latina American professionals, the study explores how the absence of innovative, internal-facing mobility systems affects professional trajectories, emotional well-being, and organizational commitment. The findings reveal that organizations incur significant costs when internal talent is overlooked, including erosion of trust, disengagement, loss of institutional knowledge, and weakened leadership pipelines. Participants described an internal mobility paradox in which investments in education, skill development, and leadership training were disconnected from promotion and decision-making processes, prompting many to seek advancement externally. Grounded in internal labor market theory, the knowledge-based view of the firm, and signaling theory, the study demonstrates how organizations that fail to actively identify, engage, and elevate internal talent forfeit strategic advantages embedded in employee expertise and commitment. The findings underscore the need for innovative, internal-first talent management systems that surface employee capabilities, strengthen retention, and transform organizational cultures from reactive hiring to sustainable leadership development.

Tariff Refunds and the Consumer Justice Gap: The Tariff Refund Illusion, Distributional Injustice, and the Limits of Importer-Centric Trade Remedy Law

2026-04-20T15:06:12+00:00

On February 20, 2026, the United States Supreme Court ruled 6-3 in Learning Resources, Inc. v. Trump, Nos. 24-1287 & 25-250, 607 U.S. ___ (2026), that tariffs imposed under the International Emergency Economic Powers Act (IEEPA) exceeded statutory authorization. The ruling immediately sparked a debate over refunds for an estimated $133.5 billion in assessed duties and up to $175 billion in total exposure. This commentary argues that this debate is fundamentally misframed: it conflates legal obligation, which remitted duties, with economic burden, which absorbed the cost. Drawing on tax incidence theory, distributional economics, sociological frameworks of structural inequality, and supply-chain analysis, this commentary establishes that between 86 and 96 percent of the IEEPA tariff burden was borne by U.S. firms and consumers, with tariff-attributable household costs estimated at $1,500-$1,800 in nominal 2025 dollars. The commentary further demonstrates that tariffs operate as a regressive fiscal instrument, disproportionately burdening low-income households, communities of color, and small businesses, and that the emerging refund architecture replicates this asymmetry by channeling restitution exclusively to importers and large businesses. This systematic disjunction between legal and equitable restitution is theorized as the consumer justice gap. The commentary concludes with a policy framework that encompasses conditional refund statutes, income-adjusted consumer tax credits, and Federal Trade Commission oversight, all designed to align restitution with economic harm.

The Ethical Dimensions of Remote Work: Surveillance, Privacy, and the Impact of Technostress in Digital Work Environments

2026-04-20T15:09:35+00:00

The rapid transition to digital work environments has fundamentally altered the psychological contract between employers and employees, exposing significant ethical vulnerabilities in modern management practices. This paper analyzes the moral implications of decentralized labor, focusing specifically on the expansion of electronic performance monitoring and algorithmic control, alongside the resulting erosion of spatial and temporal boundaries. By substituting physical oversight with continuous digital surveillance, organizations transform the domestic sphere into a visible corporate asset, heavily compromising worker autonomy and privacy. Furthermore, the institutional expectation of constant connectivity disrupts the cognitive recovery process, directly engineering technostress and emotional exhaustion among the workforce. These psychological burdens represent structural defects in work design mandated by management rather than individual vulnerabilities. The analysis argues that alleviating technology-induced exhaustion is a fundamental ethical obligation for organizational leadership. To ensure the long-term sustainability of remote work, executives must transition from behavior-based micromanagement to output-based evaluation and implement strictly enforced right-to-disconnect policies, prioritizing mutual trust over invasive tracking mechanisms.

Cognitive Load and Trust in Automated Vehicle (AV) Handoffs: Psychological Determinants of Safe Takeover Transitions

2026-04-20T15:12:03+00:00

The rapid evolution of automated vehicles (Society of Automotive Engineers [SAE] Level 3) presents emerging safety challenges during Takeover Requests (TORs), when operational control shifts from automation to the human driver. This interdisciplinary literature review examines how cognitive load, stress, vigilance, and trust in automation jointly influence takeover preparedness, reaction time, and control performance. Drawing on recent simulator and real-world studies, the review integrates evidence through the frameworks of human-automation interaction, trust calibration, and situational awareness theory. Results indicate that elevated cognitive workload and reduced vigilance are often caused by non-driving-related tasks delay driver responses and impair lane-keeping. Excessive stress and over-trust, further impair situational awareness and motor control, whereas ideal performance occurs under moderate stimulation, reasonable trust, and sufficient TOR lead time. Evidence also shows that multi-channel TOR designs (visual and auditory) and driver-state monitoring systems improve response reliability. The review positions takeover safety as a socio-technical phenomenon shaped by human cognitive, emotional, and attentional states as much as on system design. Recommended interventions include adaptive timing of TOR, trust-regulated system transparency, and targeted training to restore situational awareness. Despite rapid technological advancement, research on the psychological demands of supervisory driving remains limited. Understanding how automation reshapes trust, stress, cognitive workload, distraction, and well-being is essential for developing human-centered strategies that support driver readiness, safety, and long-term acceptance of automated vehicle systems.

Explaining and Advancing Information Technology Business Value (ITBV) in Healthcare from a Socio-Technical Systems Perspective

2026-04-20T15:14:50+00:00

Healthcare organizations increasingly invest in artificial intelligence (AI) and telehealth technologies to improve quality, efficiency, and performance. Yet the realization of information technology business value (ITBV) remains inconsistent. This commentary argues that digital value in healthcare does not stem from technological sophistication alone but from deliberate integration within socio-technical systems that cultivate calibrated trust in automation. Drawing on established ITBV and socio-technical theory, the paper reframes AI and telehealth as capability-building infrastructures whose effectiveness depends on participatory governance, clinician engagement, and stakeholder trust. Illustrative cases, including AI-enabled sepsis detection, remote patient monitoring, and AI-assisted radiology, demonstrate that adoption and impact hinge on workflow alignment, interpretability, transparency, and professional identity considerations. Technological resistance is repositioned as a rational and informative response to misalignment rather than obstruction. The inquiry advances a people-centered framework emphasizing engagement, adaptive leadership, and structured oversight as essential conditions for sustainable digital transformation in complex healthcare ecosystems.

Perceptions of Crime-Specific Recidivism Rates: A Comparative Analysis of U.S. Citizens and International Students

2026-04-20T15:23:40+00:00

Recidivism, defined as the tendency of formerly incarcerated individuals to reoffend, remains a persistent challenge for the criminal justice system and carries important implications for public safety, resource allocation, and community stability. This study examines recidivism patterns across offense types and considers key factors linked to reoffending, including barriers to successful reintegration, mental health concerns, and prior criminal history. According to data from the U.S. Bureau of Justice Statistics, property offenders exhibit the highest recidivism rates, with approximately 78% rearrested within five years, compared with 65% of violent offenders. To further explore perceptions of crime, a 2 x 2 factorial between-subjects design was used with a sample of 100 participants from a Midwestern university, including 50 international students and 50 U.S. citizens, with equal numbers of males and females in each group. Independent samples t-tests revealed statistically significant differences in fear of crime by both gender and nationality, while F-test results indicated significant differences in variance across groups. Overall, the findings suggest that gender has a more consistent influence on fear of crime than nationality, with greater variability observed among international participants. These results highlight the importance of comprehensive rehabilitation and reintegration strategies that address the complex challenges faced by formerly incarcerated individuals. They also underscore the need for a deeper understanding of public perceptions of crime. By contributing to the study of recidivism and related factors, this research supports the development of more effective, evidence-based criminal justice policies aimed at promoting community safety.

The Case Study: Digital Transformation and Equity in Public Safety Systems

2026-04-20T15:26:48+00:00

Municipal governments are increasingly adopting digital technologies to modernize public safety systems; however, emerging research indicates that these initiatives often fail to address underlying structural inequities. This case study examines the implementation of automated traffic enforcement technologies in a local city government following documented discriminatory policing practices. The objective of this study is to evaluate whether digital transformation, specifically through automated enforcement, effectively reduces bias or inadvertently reproduces existing disparities. Using a narrative literature review methodology, this analysis integrates interdisciplinary research from algorithmic governance, public administration, public health, and procedural justice to contextualize the case within broader empirical and theoretical frameworks. Findings indicate that while automation reduces individual discretion, it does not eliminate systemic bias when governance structures, policy design, and deployment strategies remain unchanged. Disproportionate citation patterns, inequitable camera placement, and revenue-driven performance metrics demonstrate that the initiative replicated rather than resolved existing inequities. The study concludes that the program's central failure was not technological but conceptual, rooted in a misunderstanding of bias as an individual rather than a structural phenomenon. As a result, the paper proposes a phased reform strategy emphasizing equity-centered governance, including transparency, community engagement, performance realignment, and continuous evaluation. The overall contribution of this study lies in demonstrating that effective digital transformation in public safety requires integrating ethical governance frameworks rather than relying solely on technological substitution, offering a model for more equitable and accountable public sector innovation.

Generative Artificial Intelligence in Professional Development: An Integrative Review of Its Influence on Coaching, Mentoring, and Advising Across Industries

2026-04-20T15:29:10+00:00

Generative artificial intelligence (AI) is rapidly changing how professionals receive support for learning, growth, and performance. This integrative systematic literature review examines how generative AI influences coaching, mentoring, advising, and related developmental roles across industries. Drawing on recent peer-reviewed research, the review identifies current uses of generative AI, its benefits, and the challenges that accompany its adoption. The analysis also clarifies key differences and similarities among coaching, mentoring, and advising to explain how AI interacts with each role. Findings show that generative AI can expand access to guidance, personalize feedback, reduce routine workload, strengthen reflection, and support self-regulated learning. At the same time, risks such as over-reliance, reduced human connection, ethical concerns, privacy issues, and limited ability to navigate complex interpersonal situations remain significant. Based on these themes, the paper proposes an original framework describing how human support and generative AI can work together in future professional development models. Recommendations for practice and future research are also provided.

Geopolitical Escalation as a Systemic Business Shock: Energy Markets, Supply-Chain Fragility, and Migration Spillovers in the Global Economy

2026-04-20T15:32:03+00:00

Escalating military confrontation in the Gulf and adjacent maritime corridors has re-emerged as a defining source of systemic risk to the global economy. While geopolitical analyses typically emphasize security and diplomacy, scholarly attention has been less directed to how regional conflict propagates through global business systems. This commentary examines how Gulf escalation transmits macroeconomic instability and population movements through three interdependent channels: global energy markets, international supply-chain networks, and migration systems. Drawing on recent peer-reviewed research (2021-2026), multilateral institutional analyses, and contemporaneous conflict documentation, the paper synthesizes insights from international business, supply-chain operations, and political economy. The 2023-2025 Red Sea crisis serves as the primary empirical foundation, documenting approximately a 70% reduction in Bab-el-Mandeb transit volumes, freight rate increases of up to 400% on key corridors, projected global inflation increments of up to 0.23 percentage points for 2025, a 90% decrease in Red Sea container shipping between December 2023 and February 2024, and a deepening humanitarian crisis affecting Yemen’s 4.8 million internally displaced persons. These documented patterns demonstrate how localized asymmetric conflict generates system-wide economic disruption. The kinetic escalation on February 28, 2026, involving Iran, the United States, and Israel provides real-time validation of the framework’s predictive claims, thereby simultaneously activating the energy, supply-chain, and migration channels theorized herein. The commentary advances four testable theoretical propositions and a research agenda that connect geopolitical risk to energy price formation, supply-chain network fragility, migration-linked labor market outcomes, and the nonlinear, interactive effects across all three channels. It advances international business scholarship by conceptualizing geopolitical escalation as a multichannel, systemic-level shock transmitted through interdependent markets, and by integrating migration and labor mobility as central business-relevant outcomes. Actionable implications are developed for multinational enterprises, international financial institutions, and policymakers seeking to construct a risk-smart global trade architecture.

Forecasting Short-Term Stock Returns Using Large Language Models: A Comparative Analysis with Human Analysts

2026-04-20T15:36:03+00:00

This study aims to determine whether artificial intelligence can accurately predict short-term returns of large-cap stocks following large announcements or significant news. ChatGPT and Gemini were asked to predict the performance of certain stocks over a week period and over a month period. Additionally, professional analyst data and predictions were compared to AI’s results. Results showed that both AI and analysts performed poorly, with AI slightly beating the analysts. These findings suggest that short-term investing as a whole may not be possible to predict, but also that access to past market patterns is helpful to create a forecast. Future research should examine smaller stocks, longer periods of time, and the use of AI with human judgment.

Malvertising as a Vector for Cognitive Hacking: Integrating Technical and Psychological Defense Using the DISARM Framework

2026-04-20T15:39:18+00:00

Malvertising, which embeds malicious code and psychological manipulation in digital ads, has become a major cybersecurity problem as it converges with cognitive hacking and disinformation. In 2024, incidents rose 10% year-over-year, with 29% involving misleading product offers, up from 26% the prior year, reflecting more deceptive tactics. Mobile environments face growing risks, as adware accounted for 35% of mobile threat detections in 2024. This qualitative study uses thematic analysis of recent industry data and academic reports to evaluate adaptive attack tactics and defense frameworks, including DISARM (Disinformation Analysis and Risk Management). Findings expose major gaps in governance, threat intelligence sharing, and the integration of technical and psychological defenses. The research concludes that a multidisciplinary strategy is vital to strengthen digital advertising ecosystem resilience and improve user protection. This work will interest cybersecurity professionals, researchers, policymakers, and digital advertising leaders seeking effective responses to emerging online threats.

Counterfeit Pharmaceuticals and Illegal Online Pharmacies as Cyber-Enabled Organized Crime and a Dark Public Health Risk

2026-04-20T15:41:28+00:00

The rapid digitization of healthcare delivery, accelerated by the COVID-19 pandemic, has expanded access to medications while simultaneously enabling the proliferation of illegal online pharmacies and counterfeit pharmaceuticals. This study reframes the issue as a form of cyber-enabled organized crime, integrating perspectives from criminal justice, forensic cyberpsychology, public health, and regulatory policy. Drawing on data and guidance from federal agencies, professional organizations, and international bodies, the research identifies how illicit actors exploit digital platforms, regulatory fragmentation, and consumer vulnerabilities to distribute falsified medications at scale. The study advances a unified analytical framework that emphasizes three interdependent drivers of this illicit market: affordability pressures, platform-level accessibility, and gaps in digital enforcement. From a forensic cyberpsychology perspective, the findings highlight how offenders leverage cognitive biases, trust heuristics, and financial stress to manipulate consumer behavior, thereby increasing victim susceptibility. From a criminal justice standpoint, the structure and operation of illegal online pharmacies align with transnational organized cybercrime, characterized by decentralized networks, adaptive tactics, and high-profit, low-risk conditions. The research proposes a comprehensive intervention model consisting of 30 practical recommendations, 30 innovative strategies, and a prioritized top 10 high-impact framework. Key strategies include platform-level verification, real-time transaction monitoring, integrated national verification systems, clinician engagement in medication source screening, and affordability-focused interventions. The study concludes that effective mitigation requires coordinated, system-level responses that simultaneously reduce consumer demand, disrupt criminal infrastructure, and enhance digital guardianship. These findings contribute to the development of interdisciplinary policy and enforcement approaches capable of addressing the evolving risks of counterfeit pharmaceuticals in a global digital marketplace.

False Negatives at Scale: Governing AI-Enabled Applicant Screening Under Digital Transformation

2026-04-20T15:44:18+00:00

Digital transformation has reshaped talent acquisition by embedding artificial intelligence (AI) within applicant tracking systems (ATS) to enable high-volume screening. However, these systems may systematically exclude qualified applicants before human evaluation occurs. This paper examines false-negative outcomes in AI-enabled screening as a socio-technical governance failure rather than a purely technical limitation. The objective is to analyze how automated screening produces exclusion errors and to identify governance controls that improve decision quality. Using a qualitative, case-based analytical approach informed by interdisciplinary literature, the study treats automated screening as a multi-stage decision process comprising résumé parsing, matching, and threshold-based filtering. The analysis demonstrates how errors in candidate representation, interpretation, and threshold calibration collectively amplify small inaccuracies into large-scale exclusion outcomes. The findings show that false-negative exclusions are primarily driven by insufficient validation, calibration, and oversight, rather than isolated technical defects. This paper reframes ATS failures as governance breakdowns and outlines a structured approach for aligning system design, organizational processes, and oversight to improve hiring outcomes and accountability.

Federal-State Conflict in Medicaid Governance: A Policy Analysis of the Trump Administration's 2026 Minnesota Medicaid Funding Withholding Actions and Implications for State Health Systems

2026-04-22T03:55:55+00:00

Medicaid, the joint federal-state health insurance program for low-income Americans, covered an estimated 80 to 85 million individuals nationally as of early 2026, following the post-pandemic enrollment unwinding, and remains one of the largest intergovernmental fiscal partnerships in U.S. history. The Trump Administration’s 2026 series of escalating funding withholding and deferral actions against Minnesota’s Medicaid program, with a cumulative potential annual exposure exceeding $2.26 billion as of March 2026 represents, according to Minnesota’s filed federal complaint, an unusually large and highly contested use of federal Medicaid payment deferral authority described by the state as without precedent in categorical scope . This qualitative policy analysis examines the chronology, legal basis, empirical justification, and projected consequences for the health system and population health of these actions. Drawing on primary government sources, federal court filings, peer-reviewed health policy literature, and federal agency data, this paper argues that the administration’s funding withholding approach diverges from established cooperative federalism norms and the administrative law framework governing Medicaid compliance enforcement, creates disproportionate harm to clinically vulnerable beneficiary populations, and generates serious fiscal instability for a state whose 2025 Payment Error Rate Measurement (PERM) finding of 2.2% was substantially below the national rolling rate of 6.12%, though CMS cautions that state-specific PERM rates are not directly comparable across states due to methodological variation. Critically, the most prominent fraud case cited by the administration, Feeding Our Future, involved a federal child nutrition program, not Medicaid healthcare or insurance, raising serious questions about the analytic basis for applying Medicaid funding penalties in response to fraud in a programmatically distinct federal initiative. Integrating the health policy, organizational, and constitutional law literatures, the paper advances ten actionable policy recommendations that address fraud governance, intergovernmental fiscal relations, and the structural protection of Medicaid beneficiaries against deployment of conditional spending authority under contested legal circumstances.

Understanding the Complexities of Off-Label Prescription Drugs Prescribing in Healthcare

2026-04-22T04:01:46+00:00

Off-label prescribing remains an essential yet complex component of modern clinical practice, enabling clinicians to address unmet therapeutic needs in the absence of approved treatment options. Despite its widespread use, this practice introduces significant public health, ethical, and professional challenges that are insufficiently explored in existing literature. This commentary examines off-label prescribing through a risk management and patient safety lens, emphasizing the interplay between clinical autonomy, fragmented evidence, and systemic vulnerabilities in care coordination. Drawing on interdisciplinary theoretical frameworks, including patient-centered care, interprofessional collaboration, and systems-based risk models, this analysis reframes off-label prescribing as a high-risk system behavior rather than an isolated clinical decision. The perspectives presented highlight critical gaps in pharmacovigilance, underreporting of adverse events, and inconsistencies in informed consent and documentation practices. Particular attention is given to coordination of care risks, where fragmented communication among providers may lead to duplication of therapy, inadequate monitoring, and increased patient harm. In response, this commentary advances practical, system-level strategies, including the implementation of active surveillance systems, pharmacist-integrated care models, standardized communication protocols, and enhanced patient engagement through shared decision-making and real-world data collection. Policy and regulatory implications are also examined, emphasizing the need for adaptive, evidence-responsive frameworks that balance clinical innovation with accountability. Recommendations include strengthening data interoperability, incentivizing evidence generation for off-label uses, and formalizing oversight mechanisms for high-risk prescribing. Collectively, this work contributes to a more comprehensive understanding of off-label prescribing as a multidimensional issue and proposes a structured, risk-informed approach to improving patient safety while preserving clinical flexibility.

Epistemological Gatekeeping in Technology Startup AI Adoption: A Narrative Review of Epistemic Injustice, Concentrated Leadership, and Algorithmic Bias

2026-04-22T04:08:15+00:00

Technology startups are adopting artificial intelligence at an increasing pace, yet smaller firms are more than twice as likely as larger organizations to lack governance roadmaps or dedicated teams for overseeing AI adoption. In startups with 10 to 100 employees, a small number of founders and C-suite leaders hold concentrated authority over AI decisions, functioning as epistemological gatekeepers who determine whose knowledge shapes adoption choices and whose perspectives are excluded. This narrative literature review integrates scholarship across three fields that have developed largely in isolation: Miranda Fricker’s epistemic injustice theory, entrepreneurship research on startup decision-making and governance, and critical AI studies examining algorithmic bias. The review identifies particular organizational mechanisms through which epistemic injustice operates in startup AI governance. These include inferential inertia, temporal shifts in credibility, hermeneutical closure, and the active promotion of dominant epistemological assumptions by AI systems themselves. Results show that epistemological gatekeeping in startups is not a matter of individual bias but rather a structural phenomenon arising from the interaction of concentrated authority, informal governance, speed culture, resource scarcity, and demographic homogeneity. The review concludes that bias mitigation is fundamentally an epistemological problem requiring attention to whose knowledge informs AI decisions, and recommends phenomenological research to investigate how startup leaders experience and enact their gatekeeping role.

Applying Analytics to Fireground Rescue: Identifying Effective Strategies Through Large-Scale Data

2026-04-22T04:11:40+00:00

This study applies advanced analytical techniques to examine firefighter rescue operations using a large-scale dataset of real-world incidents. The purpose is to identify effective strategies that enhance operational performance and improve life-saving outcomes on the fireground. Drawing on firsthand reports from firefighters involved in actual rescues, the study analyzes 5,074 documented incidents, representing over 250,000 data points collected as of January 1, 2026. The dataset captures a wide range of variables, including incident conditions, environmental factors, victim characteristics, and tactical decisions. Using quantitative methods, the analysis identifies patterns, key predictors, and relationships associated with successful rescue outcomes. Particular attention is given to decision-making under high-risk conditions, resource allocation, and timing of interventions. To ensure data quality, the Firefighter Rescue Survey (FRS) has undergone multiple iterations over a six-year period, improving reliability and relevance while incorporating practitioner feedback. Although some variables reflect smaller subsamples due to survey evolution, the dataset remains robust for large-scale analytical assessment. Findings from this study contribute to the development of evidence-based practices in fireground operations by translating complex data into actionable insights. The results have implications for firefighter training, operational planning, and policy development, ultimately supporting improved efficiency, reduced risk, and enhanced survival outcomes in emergency response environments

Digital Transformation and Algorithmic Hiring: Evaluating AI Recruitment Failures in Biotechnology Talent Acquisition

2026-04-22T04:17:01+00:00

The rapid adoption of artificial intelligence (AI) in recruitment marks a significant transformation in how organizations identify, evaluate, and select talent. Although AI-driven hiring systems offer efficiency, cost reduction, and standardized decision-making, their implementation may introduce organizational risks if technological capabilities do not align with complex workforce requirements. This case study analyzes a global biotechnology company that adopted an AI-based applicant screening system as part of a broader digital transformation initiative. The investigation reveals that algorithmic screening tools inadvertently excluded highly qualified candidates for specialized research and development positions, thereby narrowing the candidate pool despite ongoing staffing shortages. Drawing on peer-reviewed literature from databases such as Google Scholar and ProQuest, the analysis employs a narrative literature review to assess the organizational drivers, benefits, and risks of AI-enabled recruitment. The findings demonstrate that keyword-based screening models, limitations in training data, and insufficient human oversight contributed to algorithmic bias and false-negative screening errors. These results show that automated hiring systems can undermine strategic talent acquisition in knowledge-intensive industries when organizational processes and technological tools are not effectively integrated. The study applies strategic and change management frameworks, including the McKinsey 7S model and Kotter’s change model, to interpret the organizational dynamics underlying the technology failure. The results underscore the importance of sociotechnical integration, governance structures, and leadership oversight to ensure that AI recruitment systems support rather than replace human judgment. The study concludes with practical recommendations for organizations seeking to implement AI-enabled hiring technologies responsibly while maintaining access to diverse, highly specialized talent in scientific and innovation-driven sectors.

The Limits of Palliative Care in Relation to Patients’ Rights

2026-04-22T04:20:52+00:00

Palliative care has become one of the most important fields of contemporary medicine, as it seeks to relieve suffering and improve the quality of life of patients facing life-threatening illnesses. Unlike strictly curative approaches, palliative care integrates physical, psychological, social, and spiritual dimensions, with the primary goal of preserving human dignity. However, the development of palliative care has generated numerous dilemmas regarding the limits of medical intervention and the relationship between professional obligations and patients’ rights. This paper analyzes the main rights of patients in the palliative context, including the right to information, informed consent, pain control, dignity, and decisional autonomy. It highlights the difficulties that arise when patients refuse treatment, request the discontinuation of invasive interventions, or, conversely, wish to continue procedures that no longer provide real benefits. The study also examines the frequent conflicts between the patient’s wishes, family expectations, and the responsibilities of healthcare professionals. The paper argues that the limits of palliative care are not exclusively medical, but also legal, ethical, social, and economic. The absence of a clear legal framework regarding advance directives, palliative sedation, or the refusal of treatment can generate tensions and uncertainty. At the same time, unequal access to palliative care services and the lack of adequate resources affect the effective exercise of patients’ rights. The main conclusion is that palliative care must be grounded in respect for patient autonomy, dignity, and values, avoiding both therapeutic abandonment and therapeutic obstinacy. A balanced approach requires not only symptom control, but also the protection of the fundamental rights of the person in the face of illness and death.

Addictions Between Pleasure and Pain: An Interdisciplinary Approach

2026-04-22T04:30:53+00:00

This article examines the dynamics of pleasure and pain in the context of addiction from an interdisciplinary perspective, focusing on the neurobiological, psychological, and theological mechanisms involved. Addiction is a disorder of the brain’s reward system and can result from a pursuit of pleasure that is misdirected toward a substance or behavior. The neurobiological perspective highlights the role of dopamine in motivating and sustaining addictive tendencies, showing how pleasure is gradually transformed into pain when sought in substances such as drugs, alcohol, or compulsive behaviors. The psychological perspective captures this mechanism in the “pleasure principle,” which highlights the human pursuit of pleasure and avoidance of pain, thus illustrating the inner experience of the addicted person. From a theological perspective, addictions are viewed in terms of sin or passions and represent a distorted orientation toward pleasures that lead to pain, while a reorientation of the self toward a relationship with God opens the possibility of experiencing authentic joy. By analyzing this aspect of addiction, it becomes clear that orienting toward the disordered pleasures associated with substances and behaviors can be countered by the pursuit of spiritual pleasures that can be experienced in communion with God and through the development of virtues.

Love Beyond Algorithm. Why Will AI Never Be Conscious?

2026-04-22T04:34:40+00:00

The subject of AI consciousness is becoming increasingly relevant as rapid technological developments unfold. This paper argues that artificial intelligence will never attain what can be defined as genuine consciousness. The study begins by defining the nature of real consciousness, which is described as profoundly experiential and relational. Several main arguments against the idea of AI consciousness are examined, with special reference to John Searle’s Chinese Room thought experiment, the implications of Kurt Gödel’s incompleteness theorems as interpreted by Roger Penrose, and the problem of qualia articulated by Thomas Nagel. The final part of the paper maintains that consciousness goes beyond the cognitive level and is a constitutive part of ontological relationships. Building on Martin Buber’s I–Thou framework and reflections from theological anthropology, it argues that love is an ontological dimension of personal being. According to Christian theology, this love is perfectly exemplified in Jesus Christ. Because artificial intelligence is confined to computational programming, the kind of subjectivity required for genuine relational selfhood cannot arrive. Therefore, AI can mostly remain a simulation rather than a conscious entity.

Mutual Aid After 1989. The Kolping Model in Romania

2026-04-22T04:37:42+00:00

Following the fall of the communist regime in 1989, Romania faced major social and economic challenges stemming both from structural changes in society and from the mentalities formed during the communist era. In this context, Kolping International and its European network initiated a complex process to support the transformation processes in the Central and Eastern European countries, providing emergency material aid and gradually evolving toward a “help for self-help” development model based on the establishment of local associations and their projects. In this context, the process of building civil society required an investment on social structures that were unknown after the communist era. Another objective was to launch enterprises that would offer new perspectives to the disintegration of the large state-owned conglomerates. The article analyzes the stages involved in building a network-based mutual aid social structure composed of associations and their enterprises. This article outlines the conditions under which such a social structure can become functional. It analyzes the complete cycle in which the recipient of emergency aid achieves a level of autonomy and can become a donor. The transition from a community dependent on aids and expertise to an independent structure that works efficiently and is self-sustaining (Association Journal, 2018) is relevant to the entire spectrum of association networks established in Central and Eastern Europe by the partners from Western Europe.

Contemporary Ideological Transformations and the Effects on the Family

2026-04-22T04:40:56+00:00

The paper analyzes contemporary ideological transformations and their effects on the family, with a special emphasis on the relationship between new cultural paradigms and the Christian vision of the person, marriage, and family life. The study first outlines the historical and conceptual framework of these transformations, highlighting the way in which certain ideas developed in modern philosophy, sociology and psychology have influenced the redefinition of identity, social roles, and human relations. Furthermore, the research examines the distinction between the biological and socio-cultural dimensions of the person, as well as the implications of this separation for understanding human nature and family structure. From a Christian perspective, the family is presented as a space of communion, self-sacrificing love, and moral and spiritual formation, with a foundation that surpasses simple social convention. The paper also highlights the tension between the individualism promoted by some contemporary currents and the Christian conception of responsibility, complementarity, and fidelity. The main conclusion is that current ideological transformations profoundly influence the understanding of the family, but these also offer an opportunity to reaffirm fundamental Christian values and rediscover the authentic meaning of family life.

Ancon Technologies Case

2026-04-22T04:44:15+00:00

This case study examines the organizational, operational, and ethical challenges Ancon Technologies faced during its digital transformation when implementing an artificial intelligence (AI)-driven applicant screening system. In particular, the study focuses on how this company's reliance on keyword matching and historical hiring data unintentionally excluded highly qualified candidates, delayed the filling of critical research positions, and weakened its ability to compete for them within the biotechnology industry. The objective of the research is to identify the design, governance, and oversight improvements needed to better align AI-enabled recruitment systems with organizational talent acquisition goals. A qualitative, literature-based case study methodology was used to analyze the problem within its strategic and technological context. A secondary research approach was used to inform the analysis, drawing from one peer-reviewed journal article, scholarly books, and academic literature on digital transformation, artificial intelligence in recruitment, ethical governance, and change management. Using the Strategic Alignment Model, Digital Transformation Theory, and Kotter's Eight-Step Change Model, the study identifies and addresses the root causes of recruitment failure. There is a growing body of evidence showing that misalignment among algorithm design, training data, governance practices, and workforce needs can compromise the effectiveness of recruitment and the effectiveness of an organization. The case highlights that overreliance on automated decision-making without sufficient human oversight increases the risk of bias, exclusion, and poor hiring outcomes. The study concludes that organizations can improve AI-driven recruitment by broadening evaluation criteria, updating training data, strengthening governance frameworks, and embedding human judgment into decision-making processes. This study demonstrates the importance of aligning technology, ethics, and strategy to support fair, effective, and sustainable talent acquisition through the responsible implementation of AI.

The Influence of Music on Memory: Cognitive Mechanisms, Educational Applications, and Therapeutic Benefits

2026-04-22T04:47:20+00:00

The interaction between music and memory has been of great interest in cognitive science and psychology, disclosing mechanisms through which auditory stimuli affect retention of information. This review presents recent research on the distinctive influence of music on short- and long-term, working, visual, auditory, and mechanical memory. Research suggests that music can enhance memory by improving concentration and mood. However, its influence varies depending on factors such as tempo, complexity, and familiarity. While music frequently facilitates memory and performing tasks, certain types of music may interfere with a person's ability to concentrate and engage their brain. It has also enhanced memory in both educational settings and therapeutic practices, which can be beneficial for both healthy individuals and those suffering from memory impairments. In addition, music therapy also shows promise in treating memory loss in patient populations, specifically those suffering from neurodegenerative diseases like Alzheimer's. The influence of music on memory is further modulated by individual differences, such as musical training and personal preferences. The present paper focuses on the potential use of music in educational systems and therapeutic interventions for improving cognitive health and memory retention in humans.

Digitalization of Reserve Management in NATO/EU: Record-Keeping, Call-Up, Distributed Training, and Cyber Risks

2026-04-22T04:49:51+00:00

Military reserve management is undergoing a structural transformation driven by accelerated digitalization and interoperability requirements within NATO and the European Union. This article critically examines reserve personnel record-keeping and mobilization architectures, distributed training models based on the Live-Virtual-Constructive (LVC) paradigm, and cyber risks specific to digital reserve management platforms. By comparing centralized and federated data management models, the paper identifies opportunities to reduce force generation cycle times while highlighting vulnerabilities associated with digitalization, including the compromise of databases containing reservists’ personal information and denial-of-service attacks during critical call-up periods. The analytical framework integrates European legal requirements (GDPR, NIS2) with NATO standards (STANAGs) and proposes a governance model oriented toward resilience and operational continuity. The conclusions offer eight prioritized recommendations for strategic, technical, and operational decision-makers.

Probation Pathways and Youth Incarceration: A Statistical Case Study of the California Juvenile Justice System

2026-04-22T05:03:28+00:00

This data-driven case study investigates the impact of probation pathways and youth incarceration on outcomes within the California juvenile justice system. Juvenile probation outcomes are influenced by multiple factors, including race/ethnicity, age, education level, probation assignment, completion rates, and recidivism, as identified through a systematic review and analysis of juvenile justice data. Utilizing archival data from the California Department of Justice, the California Juvenile Court and Probation Statistical System (JCPSS), the California Justice Data & Investigative Bureau, and the California Criminal Justice Statistics Center, this study examines juvenile arrests, referrals, petitions, and adult court dispositions spanning 2022–2024. Building on prior research, including Youth Behind Bars: A Data-Driven Case Study on Juvenile Incarceration in California, this study emphasizes probation both as an intervention and as an alternative to confinement. The analysis aims to identify factors that influence probation decisions, explore strategies for minimizing unnecessary formal system involvement, and evaluate practices that effectively support youth through personal development, positive behavior change, and long-term success. Findings inform recommendations for a juvenile justice framework that prioritizes community-based probation, reduces reliance on incarceration, reinvests in rehabilitative services, and addresses systemic inequities within California’s juvenile justice system.

Breakfast Composition and Its Impact on Cognitive Performance in Students’ Memory

2026-04-22T05:51:31+00:00

Breakfast is frequently regarded as the most crucial meal of the day, particularly for students whose scholastic success is significantly dependent on cognitive abilities like memory. This subject is significant as numerous students either forgo breakfast or ingest nutritionally deficient meals. This study investigates the effects of various breakfast varieties on students’ memory performance. This review examines scientific research on the impact of various breakfast types on memory function. The results indicate that nutritionally balanced breakfasts, particularly those rich in protein, enhance students’ short-term memory and attention, but breakfasts heavy in sugar, deficient in nutrients, or the omission of breakfast entirely, can detrimentally affect cognitive abilities. The article indicates that both the act of consuming breakfast and the specific foods chosen are crucial for enhancing kids’ memory retention and academic success. Promoting healthful breakfasts may serve as a straightforward yet potent method to enhance academic performance by fostering increased memory.

Methodology of Psychological Profiling of the Offender: Inductive and Deductive Approaches in Investigative Practice

2026-04-22T05:56:42+00:00

Criminal profiling methodology constitutes a complex investigative framework predicated upon distinct epistemological foundations that fundamentally shape the inferential processes employed in offender identification. This paper provides a comprehensive critical analysis of inductive and deductive approaches to psychological profiling, examining their philosophical underpinnings, operational mechanisms, and practical implications within contemporary investigative contexts. The inductive methodology, grounded in statistical generalization and probabilistic reasoning, derives offender characteristics from empirical databases of previously solved cases, while the deductive approach emphasizes case-specific forensic analysis and logical inference from physical evidence. Through systematic evaluation of both American and European perspectives, this research reveals that neither methodology possesses inherent superiority; rather, each demonstrates distinctive strengths and limitations contingent upon investigative circumstances, evidentiary availability, and analytical objectives. The analysis explores fundamental epistemological tensions, including the problem of inductive incompleteness, deductive premise dependency, and the emerging role of abductive reasoning as an integrative framework. Critical examination of methodological controversies reveals persistent challenges related to standardization, validation, and the balance between empirical generalization and individualized analysis.

Rural–Urban Disparities in Traffic Fatalities in Iowa: A Local vs. State Perspective

2026-04-24T15:50:47+00:00

This study examines differences in traffic accident fatalities across local and state road systems in Iowa, with the goal of identifying key factors influencing variations in fatality rates. Iowa’s extensive roadway network—spanning state, county, municipal, and institutional roads—presents challenges for analyzing safety across jurisdictions. Using a comparative approach, this research explores patterns, risk factors, and spatial disparities associated with fatal crashes. Data were obtained from the Iowa Department of Transportation, including the Iowa Crash Analysis Tool (ICAT) and roadway inventory systems. The study analyzes approximately 25,000 crash incidents involving over 99,000 individuals between 2020 and 2024 (Iowa Department of Transportation, 2025a). Findings indicate that while state highways account for a larger share of total fatalities due to higher speeds and traffic volumes, locally controlled and rural roads exhibit higher fatality rates per crash. Contributing factors include environmental conditions, roadway design, behavioral risks such as impaired driving and seatbelt non-use, and delayed emergency response times in rural areas. The analysis highlights key differences between urban and rural crash dynamics, as well as the role of infrastructure, enforcement, and policy interventions in shaping outcomes. Results suggest that targeted strategies—such as increased investment in rural road safety, stronger impaired driving enforcement, and improved nighttime visibility—can reduce fatality risks. By integrating local and statewide perspectives, this study provides a more comprehensive understanding of road safety in Iowa and offers evidence-based insights to inform policy, enforcement, and prevention efforts aimed at reducing fatalities.