Social Sciences

Abstract: Socialcontract theories constructed by Enlightenment thinkers through rationalspeculative reasoning are highly prone to cognitive divergences stemming from researchers’ differing standpoints. Even in the twentyfirst century, scholars adopting distinct theoretical perspectives still hold markedly different interpretations of the concept of the “social contract”. Some viewpoints even categorize contemporary autocratic regimes as a form of social contract. Synthesizing multidisciplinary research findings, this paper distills and proposes the underlying contract, which is captured through the metaphor of ancient balance scales embedded within the human psyche. This paper seeks to resolve cognitive divergences arising from differences in standpoints by drawing on empirical evidence from psychological and behavioural studies, while also offering a fresh perspective for analysing a wide range of social and political phenomena.

Abstract: This article explores the strategic pathways to achieve information technology sovereignty that Bangladesh can pursue. Digital sovereignty requires coordinated national capacity across data governance, secure infrastructure, software ecosystems, artificial intelligence policy, cybersecurity, supply-chain resilience, and human capital development. The article proposes a balanced model based on open standards, trusted international partnerships, selective self-reliance, and phased institutional investment. Bangladesh can leverage its youthful workforce, growing digital economy, fintech innovation, and emerging policy frameworks to progressively reduce structural dependency while strengthening national autonomy, resilience, and competitiveness within the global digital order.

Abstract: TikTok supports fashion discovery and creator-mediated brand communication. This study examines associations among TikTok fashion-brand content involvement, brand awareness, brand engagement, and purchase-related intentions among Bulgarian users and illustrates a human-governed analytical architecture for structured and open survey data. An online survey yielded 295 responses; 268 eligible TikTok users comprised the sample. The architecture organizes primary measurement and structural analysis, secondary prediction and moderation, exploratory segmentation and text analysis, and descriptive multi-criteria synthesis within processing stages. Content involvement was positively associated with awareness (β = 0.683), engagement (β = 0.512), and purchase-related intentions (β = 0.283). Awareness was associated with engagement (β = 0.300) but had no incremental direct association with purchase-related intentions; engagement showed the strongest direct association (β = 0.496). The model explained 52.5% of purchase-related-intention variance, and PLS-PM prediction produced lower errors than the training-mean benchmark for all four outcome indicators. No demographic interaction received Holm-adjusted support. Illustrative multi-criteria synthesis ranked engagement first, involvement second, and awareness third under sample-derived criteria. Dictionary-assisted open-response categories concerning presentation, creators, reviews, demonstrations, music, and humor were manually checked against the responses. Cross-sectional self-reports, concentrated sampling, broad working constructs, and internal validation require cautious, noncausal interpretation and independent external replication.

Abstract: Large language models (LLMs) are entering decision-support systems as inexpensive synthetic experts, yet their judgments are rarely validated against published human targets. We benchmark six LLMs on a source-audited corpus of 27 published Analytic Hierarchy Process (AHP) studies in English and Korean, with a preregistered holdout of 22 studies and 21 reference-weight tasks, retaining 28,783 valid model calls across a preregistered phase and a prospectively frozen extension; inference is task-level with study-clustered uncertainty. Holdout rank replication was modest and model-dependent, and only HCX-007 clearly beat uniform weights on absolute error. A diagnostic showed that the fixed-source-order rank evaluation is confounded with criterion presentation order: the corpus’s reference vectors largely follow source listing order, and a model-free descending-order rule reaches a mean correlation of 0.735, outscoring every model tested. A prospective randomized-order experiment reversed the point-estimate pattern: HCX-007, the only model that strongly tracks presentation order, was also the only one whose accuracy declined, and a low-yield direct-recall probe found no verbatim reproduction of published weights. Persona conditioning was often weaker than repeated-draw variability. Synthetic panels can support piloting and stress-testing of decision pipelines, but replay benchmarks must randomize presentation order before rank accuracy can be read as reconstructed expert judgment.

Abstract: Technological change is commonly represented through successive inventions, technological waves, general-purpose technologies or sociotechnical transitions. These approaches identify historically significant configurations, but they do not generally provide a causal account that begins with a change in the technical delegability of a function and follows the transformation of its consequences from the organizational to the systemic level. This article develops the concept of systemic propagation: the process through which the direct effects of a change in functional delegability are transmitted and transformed across organizational and economic interdependencies until they alter constraints, opportunities or decision conditions shared by multiple actors. Propagation is distinguished from diffusion: diffusion concerns the spread of adoption, whereas propagation concerns the transmission and transformation of consequences, including effects experienced by non-adopters. The mechanism is recursive and open. Propagations can modify the organizational structures and connections through which they proceed, and similar initial changes can produce different consequences depending on the interdependencies and timing involved. Moreover, new technological capabilities frequently emerge before earlier propagations have relatively stabilized. A later propagation may amplify, redirect, combine with, absorb or inhibit an earlier trajectory. Propagations are also affected by acute perturbations, structural pressures and intentional interventions—including wars, natural disasters, climate change, migration, labour scarcity and industrial policies—which alter the resources, connections, rules and incentives through which technological change unfolds. Artificial intelligence provides a particularly consequential contemporary case: its propagation is unfolding through organizational and digital systems that are themselves products of earlier, incompletely stabilized technological propagations. The article interprets the Industrial Revolution as a unitary historical process based on the continuing expansion of technically delegable functions. Within this process, the apparent succession of technological waves is understood as the visible historical pattern generated by multiple, interacting and unfinished propagations.

Abstract: This paper deals with the joint optimization of the conditional value at risk and the expected wealth if the involved risks are comonotonic, that is, the growth of one risk will never result in a decrease in any of the others. A first contribution seems to be that the approach may allow us to control the conditional value at risk beyond the selected confidence level. As a second contribution, an explicit solution is found and it does not depend on any decision maker budget. The approach is quite general and compatible with many pricing methods. In particular, one can deal with financial methods involving risk-neutral valuation and stochastic discount factors, as well as with more complex convex methods of an actuarial nature. Two applications have been chosen. The first one deals with a mathematical finance problem, namely, the optimal portfolio insurance. It will be seen that classical portfolio insurance strategies, such as the sale of futures or the purchase of puts, are not necessarily optimal. Furthermore, if a put purchase is optimal, the role of the strike is critical. This fact should be quite relevant, at least for practitioners. The second application deals with an actuarial mathematics problem, namely, the optimal reinsurance. The study is implemented under very weak assumptions about the involved distributions a premium principles. As in the general case, the solution does not depend on any budget. This could be a significant finding, particularly in the case of optimal reinsurance, as it differs markedly from the results of previous researchers. The difference is provoked by the incorporation of a financial riskless asset that can by traded by the insurer at the same time that it purchases the reinsurance. Numerical experiments illustrate some of the obtained results.

Abstract: In the data intelligence era, social media platforms have supplemented emergency decision-making with a wealth of timely data, offering new research paradigms for emergency response. It is crucial to identify and predict the emergency material demand for reducing secondary damage during emergencies. This study aims to fill the research gap in analysing the emergency material demand using real-time social media data. We propose a method for mining an emergency material demand index from social media that takes into account both sentiment intensity and social influence. Furthermore, a real-time material demand forecasting method combined the material demand indexes and Holter-Winter procedure is designed. Using this method, we analysed 3,323,151 tweets on the Weibo Public Opinion Datasets from Dec. 1, 2019, to Apr. 30, 2020, to monitor and forecast the masks demand during the early stages of COVID-19 outbreak. For our mining method, we compare its result with the Wuhan Red Cross Society’s mask distribution data during March 2020, and the index can capture changes in mask demand during certain periods. Our forecasting method outperforms the two baseline models, with a RMSE of 2.58% and a MAPE of 3.01%. Our study provides a tool for dynamically monitoring and forecasting emergency materials demand to ensures sufficient time for the production or distribution of emergency materials.

Abstract: Natural disasters trigger diverse psychological responses, including anxiety, depression, fear, and resilience, yet disaster mental health research has largely centered on post-traumatic stress disorder (PTSD). This study investigates global patterns of mental health responses following major natural disasters using large-scale social media data. More than 450,000 publicly available posts related to floods, earthquakes, wildfires, and hurricanes between 2015 and 2025 were analyzed using natural language processing techniques, including sentiment analysis, emotion classification, and topic modeling. Distinct psychological response patterns were observed across disaster types. Wildfires generated the highest levels of anxiety-related expressions, whereas earthquakes produced intense but short-lived distress. Floods were characterized by prolonged discussions of stress and uncertainty, reflecting extended recovery challenges. Negative emotions peaked immediately after disaster occurrence and gradually declined over time, while expressions of social support and resilience increased throughout the recovery period. Regional differences further underscored the influence of socioeconomic conditions and cultural contexts on psychological outcomes. These findings demonstrate the potential of social media and artificial intelligence for real-time monitoring of disaster-related mental health. By moving beyond PTSD-focused assessments, this study provides a more comprehensive understanding of the evolution of anxiety, depression, and emotional resilience following natural disasters and offers a scalable framework to inform mental health interventions, disaster preparedness, and long-term recovery planning worldwide.

Abstract: Fairness in recommendation systems has drawn growing attention due to rising societal and regulatory concerns over algorithmic bias. Existing fairness-aware approaches typically mitigate bias by either removing sensitive attributes via representation learning or leveraging causal-path interventions (e.g., counterfactual or specific-path debiasing) to distinguish genuine causal effects from confounder-induced correlations between sensitive attributes and users perference. However, when it comes to evaluation, most prior work adopts both Demographic Parity (DP) and Equal Opportunity (EO) as simultaneous criteria, yet overlooks their inherent tension and the causal nature of the sensitive attribute. Specifically, if a sensitive attribute genuinely drives preference variation, enforcing DP forces equal exposure across groups, contradicting natural interest diversity and severely hurting accuracy; conversely, for spurious correlations, relying solely on EO fails to remove confounder-introduced bias. More importantly, these metrics are typically computed in a static, one-shot manner, ignoring that recommendation is an iterative process where even minor initial disparities can be amplified over time through feedback loops, eventually leading to substantial long-term unfairness. Nevertheless, existing studies rarely address such dynamic, long‑term fairness implications, leaving a critical gap in both evaluation and optimization. To resolve this, we propose Long-term Fairness-aware Recommendation via Adaptive Fairness Metric Selection. Our framework first learns the causal structure to identify whether the sensitive attribute has a genuine causal effect or merely a spurious association with user preferences. Based on this diagnosis, it adaptively selects the most appropriate fairness criterion: Equal Opportunity for true causality, which allows legitimate group differences in preference, and Demographic Parity for spurious correlations, which eliminates unjustified disparities entirely. The adaptively chosen metric is then integrated into an actor-critic reinforcement learning reward to optimize long-term fairness without sacrificing accuracy. Extensive experiments on Alibaba and MovieLens datasets demonstrate that proposed method achieves a superior fairness-accuracy trade-off compared to state-of-the-art baselines, and the adaptive metric selection proves indispensable for maintaining both equity and recommendation quality.

Abstract: Extreme operational environments can affect cognitive regulation, control of emotions and decision-making processes. Although stress-related impairments have been extensively studied, adaptive mechanisms supporting functional action under acute threat have also received increasing attention across several research traditions, including resilience research, stress inoculation, and studies conducted in ecologically valid operational settings. However, these findings remain dispersed across disciplines, and an integrative conceptual framework specifically addressing the adaptive mechanisms that allow certain professionals to maintain functional action under acute threat is still lacking. This concept paper proposes a theoretical framework for understanding adaptive reconfiguration in high-risk professional contexts. Drawing on literature from cognitive psychology, stress physiology, military and emergency response research, and behavioural regulation models, the article examines how individuals may preserve operational coherence despite intense physiological and emotional overload. The framework distinguishes disorganization/collapse from two other trajectories: threat-neutralization, characterized by action capacity preserved at the expense of normative control, and operational-protective, characterized by regulated action sustained by explicit normative frameworks. The paper introduces the notion of an “operational-protective trajectory” to describe forms of regulated action that remain functionally organized under extreme stress exposure. It also discusses the potential operational benefits and psychological costs associated with these adaptive configurations. By integrating findings from multiple disciplinary fields, the article contributes to a better conceptual understanding of decision-making and behavioural adaptation in high-risk environments and highlights implications for training, operational preparation, and resilience-oriented approaches.

Abstract: Purpose: To investigate the structural and operational factors responsible for Ghana’s persistent electricity supply unreliability despite substantial growth in installed generation capacity between 2016 and 2025, and to assess the potential of energy efficiency interventions to address emerging supply-demand imbalances. Design/methodology/approach: A mixed-methods approach combining descriptive trend analysis, multiple regression modelling and Energy Performance Certification (EPC)-based demand reduction scenarios was employed to examine electricity supply reliability, demand dynamics and system performance from 2016 to 2025. Findings: Installed capacity increased from about 4,100 MW in 2016 to over 5,200 MW in 2025; however, available capacity often remained below peak demand. Regression results indicate that generation outages, gas supply variability and transmission losses significantly explain electricity deficits (R² ≈ 0.78). EPC-based modelling shows that an 18% improvement in energy efficiency could eliminate peak deficits, highlighting reliability and governance challenges rather than capacity shortages. Research limitations/implications: The analysis is based on national-level sector data and assumes projected efficiency gains under EPC implementation scenarios. Practical implications: Policy efforts should prioritise fuel security, grid modernisation, institutional reforms and demand-side energy efficiency programmes. Originality/value: The study introduces EPC-based deficit modelling, demonstrating how energy efficiency can complement supply-side investments to improve electricity reliability.

Abstract: In coordination games, the distinctive presence of multiple equilibria poses a challenge to coordination, and this has led over time to the proposal of numerous determinants of the latter. In this paper, we study coordination in a repeated stag-hunt game played by two agents who are able to learn from the outcomes of each game, but do not know the payoff matrix. By modulating their learning capabilities, we show that agents are able to coordinate on the social optimum when they learn slowly from experience and are prone to making mistakes. A comparison with the results from a recent laboratory experiment is also provided.

Abstract: Social Laser Theory (SLT) provides a rigorous quantum-like mathematical formalism for modeling collective social activation. While its macroscopic effects mimic laser physics, the fundamental driver of the "lasing" regime resides in the internal state dynamics of the "social atom." In this paper, we propose a quantum-like architecture for the individual cognitive system, formalizing the social atom as a composite quantum system where mental markers—spanning cognitive, affective, and valence dimensions—are represented as internal degrees of freedom in a complex Hilbert space. We show that social activation is not a classical linear response but is governed by a Triple-Resonance Mechanism: the simultaneous quantum-like matching of semantic topic, energetic impact, and valence stance between the agent's internal state and the informational field. We detail how the repeated processing of quantized informational units ("infons") leads to a state of cognitive population inversion. We further establish a link to Human Entanglement Theory (HET), suggesting that the emergence of coherent informational fields is rooted in the phase alignment of these internal mental markers across a population. By situating SLT within the broader framework of Social Quantum Field Theory (SQFT), this paper provides a neuro-cognitive foundation for collective phenomena such as emotional contagion and rapid narrative amplification. Our results suggest that the "Social Laser" is a macroscopic manifestation of quantum-like effects across the human cognitive and neuro-social architecture.

Abstract: The incidence of technologies created with the support of federal funding at universities and federal laboratories that are transferred to the private sector is nowhere close to its potential. The natural question that this observation raises is why has technology transfer research not led to a significantly higher incidence of technology transfer? This paper argues that reification is a primary trait of the analytical frameworks used in technology transfer studies and has detrimental effects on technology transfer research. It examines how reification can adversely impact technology transfer research by distorting our understanding of technology transfer, adversely impacting descriptive research, overstating causal relationships, oversimplifying complex causal mechanisms, and obscuring the role of human agency. It presents a human-centered theoretical framework to mitigate reification. The framework presented has practical implications for technology transfer policy, practice, and research particularly policy design, organizational alignment, and metrics. The suggestions for extending the work presented in this paper focus on empirically testing the HCF and developing measurement instruments to operationalize the human-centered mechanisms of mediation in the framework. This paper contributes to the field by introducing a methodological innovation, identifying a potential flaw in widely used analytical frameworks, and exploring an under-examined topic in technology transfer research.

Abstract: Air transport of lithium-ion batteries has grown faster than the fire-protection systems designed to contain them. The United States Federal Aviation Administration verified a record 89 battery thermal events aboard commercial aircraft in 2024, representing a sixteen percent rise on the previous year, and an independent airline reporting programme recorded a forty percent increase in cargo-side incidents between 2021 and 2025. When a cell fails, it vents for several minutes before producing the visible smoke that current photoelectric detectors are built to catch. That vent gas is not a vague hazard but a chemically defined mixture of hydrogen, carbon monoxide, carbon dioxide and light hydrocarbons, almost all of which falls under Class 2 of the United Nations dangerous goods scheme. This review treats that correspondence as a deliberate design starting point, positioning the Class 2 taxonomy as a sensor architecture input rather than a filing category. The experimental literature on vent gas composition is synthesised and read against the operational record of aviation incidents. An Analytic Hierarchy Process and TOPSIS decision model is then constructed to rank five candidate sensor families—electrochemical (EC), non-dispersive infrared (NDIR), tunable diode laser absorption spectroscopy (TDLAS), metal-oxide semiconductor (MOX) and photoionisation (PID)—against seven criteria covering detection limit, response time, selectivity, flight-envelope tolerance, certification maturity, power draw and cost. The electrochemical sensor ranked first (TOPSIS closeness coefficient C* = 0.741), followed by non-dispersive infrared (C* = 0.635) and tunable diode laser spectroscopy (C* = 0.586). Robustness checks confirmed that the top-three order holds under all twenty-percent single-weight perturbations and across three policy scenarios. Because no single technology covers the full Class 2 envelope, a combined architecture is recommended: an electrochemical hydrogen channel, a non-dispersive infrared channel for the carbon oxides, and a metal-oxide array for hydrocarbon classification and redundancy. This combination aligns with the chemistry-specific findings of an independent principal component analysis of 247 reported failure cases. The review closes with concrete regulatory proposals for the ICAO Technical Instructions, the IATA Dangerous Goods Regulations and the EASA certification basis.

Abstract: Digital twins are becoming vital decision-making infrastructures across critical infrastructure sectors such as smart city urban services, transportation, energy, and healthcare. As digital twins become autonomous and gain real-time intervention capabilities, their governance becomes increasingly essential. Yet existing governance mechanisms remain largely procedural: they emphasize compliance without operationalizing the cognitive practices required to question assumptions, detect algorithmic harms, or support legitimate multi-actor deliberation. Drawing on a systematic scoping review, this study synthesizes the literature on digital twin autonomy, algorithmic risks, epistemic foundations, and governance mechanisms. The review reveals a fundamental gap: current governance mechanisms lack institutionalized cognitive capacities for continuous validation, proactive detection of emerging harms, and structured multi-stakeholder deliberation. To address this gap, the paper proposes the skeptical intelligence framework, developed through design science research. The framework integrates three cognitive functions: validation, detection, and deliberation supported by operational principles, governance artifacts, and distributed accountability roles. The framework advances digital twin governance beyond compliance toward a model rooted in critical epistemology, reflexivity, transparency, and democratic legitimacy. It also lays the groundwork for validation of the proposed framework through implementation across multi-actor digital twin infrastructure contexts—including smart city governance, port logistics, and energy networks—where DT-mediated decisions redistribute opportunities and risks across heterogeneous stakeholders. Empirical validation in an operational setting is planned as the next phase of this research.

Abstract: As the supply network for community-based elderly care services expands, the research focus shifts from mere service availability to computationally modeling who can access and utilize services effectively. Existing studies often consider accessibility as spatial distance or economic cost and equity as simple resource allocation, limiting insights into cumulative disadvantages faced by older adults with low income, digital barriers, or limited family support. Based on data from 3,800 community elder care sites across 20 U.S. metropolitan areas, individual survey data from 6,240 older adults, and community socioeconomic indicators, this study constructs a computational five-stage accessibility chain model: “Information Accessibility—Eligibility Determination—Process Accessibility—Service Availability—Outcome Attainability.”The study integrates heterogeneous data encoding, adaptive spatial accessibility computation, stage-aware vulnerability representation, and hierarchical modeling. Two-Step Floating Contour Analysis (2SFCA), stage-coupled logit modeling, and deep embedded clustering are applied to stratify risk and optimize service access prediction. A cross-vulnerability index, combining six factors—age, income, cognition, language proficiency, family support, and digital access—is incorporated into the model to quantify cumulative impacts across stages.Preliminary results indicate that inequalities do not primarily arise from geographic proximity but accumulate during intermediate phases of information identification, eligibility determination, and process navigation. Digital vulnerability and lack of family support remain major limiting factors even after controlling for spatial accessibility, demonstrating the effectiveness of stage-aware, computationally optimized modeling. This paper proposes an integrated “accessibility chain–intersectional vulnerability” computational framework, advancing service equity from outcome equity to process and transformational equity, and providing a technology-driven foundation for targeted service allocation, navigation system optimization, and identification of high-risk groups in community-based elderly care.

Abstract: Output-based indicators in entrepreneurial ecosystem governance systematically misclassify pre-threshold structural progress as policy failure, because feedback dynamics produce no immediate output signal. This study examines how institutional coordination shapes those dynamics. Using system dynamics modelling, we construct a three-stock model (active startups, entrepreneurial capabilities, and institutional support). Calibration is performed via structured expert elicitation using the Repertory Grid Technique (RGT), enabling institutionally grounded parameter estimation where comparable time-series data are unavailable. Three policy scenarios — fragmented support, financial intensification without coordination, and coordinated early intervention — are simulated for Mexico and the United Kingdom. Resource intensification alone yields only temporary gains when feedback structures remain fragmented. Coordinated intervention activates reinforcing feedback among all three stocks, enabling self-sustaining growth beyond a critical coordination threshold. The United Kingdom crosses this threshold earlier due to stronger baseline conditions; Mexico responds later but with larger proportional gains. The model provides a feedback-structural diagnostic that distinguishes pre-threshold structural assembly from genuine stagnation, with direct implications for the design of evaluation frameworks in fragile institutional contexts. RGT demonstrates potential as a calibration strategy for feedback models in data-sparse settings.

Abstract: A study of elementary counting (of simple clouds of dots by the Munduruku indigenous people of Brazil) is reanalysed in order to compare and contrast three kinds of probability mass functions (PMFs): (i) quantitative response to a discrete range of counts, (ii) the classic Poisson distribution of miscounts, and (iii) psychometric (Rasch) distributions of counting task difficulty and person counting ability. PMFs provide a means of defining — for discrete and qualitative data — the basic metrics, viz. location and dispersion, of metrology — quality-assured measurement, as increasingly required since the turn of the millenium in topical and challenging quality-assurance applications, amongst others, in the human sciences and in Artificial Intelligence. PMF-based metrics, useful in ’clinical’ and other applications where meaning and value are sought, complement the traditionally dominating role played by the corresponding probability density functions (PDF) in ’analytical’, quantitative and continuous Metrology in Physics. New insights are provided when benchmarking the Rasch Poisson Counts Model, which has received less attention in modern metrology, against full psychometric Rasch modelling.

Abstract: Across the Global South, heightened contestation over rural land is placing land administration at the centre of policy attention, as persistent mismatches between official title records and lived realities of occupancy generate legal challenges, political conflicts, and limited access to state programs. Existing systems often alienate landholders who lack valid documentation, limiting their access to welfare and compensation. Digitization of land records is frequently advanced as a solution; however, when implemented without meaningful community inclusion, it risks excluding local voices and producing inequalities in rigid and legally entrenched forms. This article critically examines whether contemporary digitization initiatives adequately address the structural challenges embedded within land administration systems, while also proposing a governance framework that addresses the institutional disconnect between policy design and implementation through decentralization, and co-governance. Drawing on qualitative research from two sites in Western India – Talasari and Chiplun – the study combines Focus Group Discussions (FGDs), field-based Key Informant Interviews (KIIs), and institutional process-mapping conducted between December 2024 and October 2025. The findings show that digitization without community-engaged implementation processes often produces inaccuracies and governance gaps, intensifying fragmentation rather than resolving it, and underscore the need for decentralized, hybrid frameworks that integrate statutory and customary systems through co-governance and community participation.