International Dataset Research Articles

Characteristics of Technological Disasters.

Disasters are the consequences of natural or technological hazards that affect a vulnerable society.1 Technological disasters are divided into three groups: industrial, transport, and miscellaneous.2,3 It is possible to determine the risks of technological disasters, to determine priorities, and to plan services by knowing this epidemiology.4 In this study, we aimed to define the distribution and characteristics of the subtypes of technological disasters in the world according to regions and years. Our study was conducted using the international dataset at www.emdat.be/. The technological disasters between 1970 and 2020, the years they occurred, their locations (region and continent), the types of disasters, and the numbers of dead and affected were recorded. We found that the greatest number of disasters occurred between 2001 and 2010. The most common type of disaster was transportation accidents. While the continent with the most frequent disasters was Asia (3 879 [45.6%]), it was followed by Africa (2 220 [26.1%]) and South and North America (1 359 [16%]). Transport accidents are the most common cause of technological disasters, and road accidents are the most common type of transport accident.

Re-imagining educational quality. The need for a multidimensional approach in evaluating educational quality through TIMSS data

In recent years, international assessments have significantly impacted the discourse pivoting educational quality, often simplifying it to a single achievement continuum. With literature emphasizing quality’s multidimensional nature, this unidimensional view challenges the concept’s complexity. Although research with international datasets has identified multiple dimensions of educational quality, they are often considered in isolation. This paper seeks to expand current state of research by advocating a multidimensional approach using cluster analysis. Employing TIMSS data, six distinct clusters are portrayed, representing diverse quality configurations across global educational systems. This nuanced evaluation enhances our understanding of quality patterns and informs recommendations for future research.

What are the key predictors of international teacher shortages?

ABSTRACT Recurrent teacher shortages have been a long-standing problem for many countries. Popular strategies to attract and retain teachers, used over several decades across the world, include bursaries, scholarships, performance-related pay, professional development and reducing workload. Governments in England have invested heavily in such policy responses, but none have been shown to be particularly effective. Such policies have tended to be based on weak research evidence. Much previous research has not considered the many different factors that can explain teacher shortages, and so yields misleading results by focussing on a few factors only. In this paper, we present our ongoing research to advance understanding of teacher shortages by comparing 18 countries that reportedly have and have not experienced teacher supply issues, using a complex Qualitative Comparative Approach, and based on numerous international datasets with (initially) hundreds of possible determinants. The results suggest that wider economic issues, such as the employment rate of graduates in subjects like humanities, are key predictors of shortages, along with teacher reports of poor behaviour of students, lack of resources, and pay. This should be of interest to a wide range of stakeholders, including policy-makers, wanting to develop more effective and targeted interventions to improve teacher supply.

Global air freight flow data for aviation policy modelling

Models of air freight are often constrained by a lack of available data. This study brings together different sources of air freight supply and demand data to address this gap. To study air freight operations, we combine schedules, flight tracking data and country-level databases of passenger and freight movements to produce estimates of global flight segment-level capacity and load factors in freighter aircraft and passenger holds for 2019–2021. To study true origin-ultimate destination air freight demand, a freight mode choice model by commodity group is developed for 2019 to fill gaps in mode information in international and national trade datasets, and estimates are made for 2019 and 2021. Initial comparisons of supply and demand data demonstrate that air freight journeys differ significantly from passenger journeys, typically including more flight legs (roughly, around 2, compared to 1.2 for passengers) and greater leg distances (2.2–2.5 times average passenger distance), with significant asymmetry in commodity flows and operations to and from individual countries and regions. These differences persist in 2021, despite COVID-19 induced shifts towards carrying more air freight in freighter aircraft. This research forms a first step towards making available an integrated database of estimated global air freight flows by commodity.

Long-term natural history in type II and III spinal muscular atrophy: a 4-year international study on the Hammersmith Functional Motor Scale Expanded.

Spinal muscular atrophy (SMA) is a genetic disorder caused by SMN1 gene mutations. Although studies on available disease-modifying treatments have reported their efficacy and safety, long-term natural history data are lacking for comparison. The aim of this prospective study was to report 4-year changes on the Hammersmith Functional Motor Scale Expanded (HFMSE) in type II and III SMA in relation to several variables such as age, functional status and SMN2 copy number. The study involves retrospective analysis of prospectively collected data from international datasets (Belgium, Italy, Spain, USA, UK). HFMSE longitudinal changes were analyzed using linear mixed effect models, examining annualized HFMSE change and its association with variables such as age at baseline, sex, motor function, SMN2 copy number. In SMA type II (n = 226), the 4-year mean change was -2.20 points. The largest mean changes were observed in sitters aged 5-14 years and the lowest in those who lost the ability to sit unsupported. In SMA type III (n = 162), the 4-year mean change was -2.75 points. The largest mean changes were in those aged 7-15 years, whilst the lowest were in those below 7 and in the SMA type IIIa subgroup over 15. Age and score at baseline were predictive of 4-year changes. Our findings provide natural history reference data for comparison with long-term follow-up of clinical trials or real-world data, highlighting the need to define patterns of changes in smaller SMA subgroups instead of reporting mean changes across an entire SMA cohort.

Toward Centrality Evaluation of Yearning Symptoms for Prolonged Grief Disorder: A Cross-Cultural Approach

Background: The new ICD-11 diagnosis of prolonged grief disorder (PGD) is characterized by the prominent role of yearning as hallmark symptom. A secondary analysis of eight international datasets on PGD was conducted to evaluate this assumption. Additionally, cross-cultural comparison explored whether the centrality of yearning differs across world regions. Methods: Primary studies originated from German-speaking countries (n = 4 samples), other European countries and Israel (n = 3 samples), as well as China (n = 1 samples). Different PGD measures were used, including yearning and longing as symptoms. For the centrality assessment of yearning, PGD symptoms were ranked by their factor loadings from confirmatory factor analyses, followed by statistical testing to determine significant differences between yearning and other symptoms of PGD in their factor loading estimates. Subsequently, ranking positions of yearning in three world regions (German-speaking, other Europe-Israel, and China) were compared. Finally, proxy thresholds for individuals at high-risk states for PGD were defined for the different datasets, and sensitivity-specificity analyses of yearning were performed. Results: Yearning was ranked high in five out of 12 models tested. In the German-speaking region, it was predominantly ranked among the most central symptoms; in the other Europe-Israel region as well as China, it tended to fall into the middle or lower rankings of symptom centrality. Sensitivity values were consistently high, while specificity values indicated moderate levels. Discussion: In line with previous research on the general outcomes of grief, the present study showed that yearning may be subject to a culture-specific distribution. Other central symptoms such as feeling as if a part of oneself died have also been shown to potentially play a central role in PGD across world regions. On the other hand, the sensitivity-specificity analyses revealed that yearning can be considered a significant (diagnostically highly sensitive) symptom for individuals in high-risk states for PGD, although it has only moderate specificity (i.e., its absence does not necessarily indicate individuals experiencing normative grief). Nonetheless, a culture-sensitive approach to psychopathology should consider the cultural differences in the centrality of this symptom group. More research is needed to better understand the role of yearning and its determinants across world regions.

Modelling crop management and environmental effects on the development of Leptosphaeria maculans pseudothecia

AbstractThe timing of ascospore release is critical in the prediction of blackleg infection, particularly if the timing of spore release coincides with early development of canola seedlings. Historically, prediction models have used average daily temperature and an environment calibration to estimate a minimum rainfall amount to trigger development. This paper describes a different approach based on hydro-thermal time, where soil evaporation and rainfall are used as a surrogate to estimate when the stubble is wet, and temperature is accumulated on an hourly basis. Furthermore, the stubble orientation due to differing harvest management practices is considered, as stubble knocked down has greater contact with the soil compared to stubble which remains in a standing position. Pseudothecia and ascospore development in the standing and lying treatments was monitored weekly to measure the rate of development in diverse environments. The new modelling approach was used to describe the pseudothecial maturation rate and predict the timing of ascospore release. Subsequently, a range of Australian and international datasets were used to assess the robustness and accuracy of this new model’s predictions. When tested across multiple locations and seasons globally, the hydro-thermal time approach had similar performance (R2 = 0.94, RMSD = 16 days) to the existing Sporacle Ezy model (R2 = 0.90, RMSD = 22 days), but without the need to calibrate in different environments and account for the delayed rate of development in standing stubble. Integrating this new approach into early warning systems for canola growers will help in the management of control measures.

Exploring uncatalogued genetic variation in antimicrobial resistance gene families in Escherichia coli: an observational analysis

Antimicrobial resistance (AMR) in Escherichia coli is a global problem associated with substantial morbidity and mortality. AMR-associated genes are typically annotated based on similarity to variants in a curated reference database, with the implicit assumption that uncatalogued genetic variation within these is phenotypically unimportant. In this study, we evaluated the performance of the AMRFinder tool and, subsequently, the potential for discovering new AMR-associated gene families and characterising variation within existing ones to improve genotype-to-susceptibility phenotype predictions in Ecoli. In this cross-sectional study of international genome sequence data, we assembled a global dataset of 9001 Ecoli sequences from five publicly available data collections predominantly deriving from human bloodstream infections from: Norway, Oxfordshire (UK), Thailand, the UK, and Sweden. 8555of these sequences had linked antibiotic susceptibility data. Raw reads were assembled using Shovill and AMR genes (relevant to amoxicillin-clavulanic acid, ampicillin, ceftriaxone, ciprofloxacin, gentamicin, piperacillin-tazobactam, and trimethoprim) extracted using the National Center for Biotechnology Information AMRFinder tool (using both default and strict [100%] coverage and identity filters). We assessed the predictive value of the presence of these genes for predicting resistance or susceptibility against US Food and Drug Administration thresholds for major and very major errors. Mash was used to calculate the similarity between extracted genes using Jaccard distances. We empirically reclustered extracted gene sequences into AMR-associated gene families (≥70% match) and antibiotic-resistance genes (ARGs; 100% match) and categorised these according to their frequency in the dataset. Accumulation curves were simulated and correlations between gene frequency in the Oxfordshire and other datasets calculated using the Spearman coefficient. Firth regression was used to model the association between the presence of blaTEM-1 variants and amoxicillin-clavulanic acid or piperacillin-tazobactam resistance, adjusted for the presence of other relevant ARGs. The performance of the AMRFinder database for genotype-to-phenotype predictions using strict 100% identity and coverage thresholds did not meet US Food and Drug Administration thresholds for any of the seven antibiotics evaluated. Relaxing filters to default settings improved sensitivity with a specificity cost. For all antibiotics, most explainable resistance was associated with the presence of a small number of genes. There was a proportion of resistance that could not be explained by known ARGs; this ranged from 75·1% for amoxicillin-clavulanic acid to 3·4% for ciprofloxacin. Only 18 199 (51·5%) of the 35 343ARGs detected had a 100% identity and coverage match in the AMRFinder database. After empirically reclassifying genes at 100% nucleotide sequence identity, we identified 1042unique ARGs, of which 126 (12·1%) were present ten times or more, 313 (30·0%) were present between two and nine times, and 603 (57·9%) were present only once. Simulated accumulation curves revealed that discovery of new (100% match) ARGs present more than once in the dataset plateaued relatively quickly, whereas new singleton ARGs were discovered even after many thousands of isolates had been included. We identified a strong correlation (Spearman coefficient 0·76 [95% CI 0·73-0·80], p<0·0001) between the number of times an ARG was observed in Oxfordshire and the number of times it was seen internationally, with ARGs that were observed six times in Oxfordshire always being found elsewhere. Finally, using the example of blaTEM-1, we showed that uncatalogued variation, including synonymous variation, is associated with potentially important phenotypic differences; for example, two common, uncatalogued blaTEM-1 alleles with only synonymous mutations compared with the known reference were associated with reduced resistance to amoxicillin-clavulanic acid (adjusted odds ratio 0·58 [95% CI 0·35-0·95], p=0·031) and piperacillin-tazobactam (0·50 [95% CI 0·29-0·82], p=0·005). We highlight substantial uncatalogued genetic variation with respect to known ARGs, although a relatively small proportion of these alleles are repeatedly observed in a large international dataset suggesting strong selection pressures. The current approach of using fuzzy matching for ARG detection, ignoring the unknown effects of uncatalogued variation, is unlikely to be acceptable for future clinical deployment. The association of synonymous mutations with potentially important phenotypic differences suggests that relying solely on amino acid-based gene detection to predict resistance is unlikely to be sufficient. Finally, the inability to explain all resistance using existing knowledge highlights the importance of new target gene discovery. National Institute for Health and Care Research, Wellcome, and UK Medical Research Council.

Framework to improve software effort estimation accuracy using novel ensemble rule

This investigation focuses on refining software effort estimation (SEE) to enhance project outcomes amidst the rapid evolution of the software industry. Accurate estimation is a cornerstone of project success, crucial for avoiding budget overruns and minimizing the risk of project failures. The framework proposed in this article addresses three significant issues that are critical for accurate estimation: dealing with missing or inadequate data, selecting key features, and improving the software effort model. Our proposed framework incorporates three methods: the Novel Incomplete Value Imputation Model (NIVIM), a hybrid model using Correlation-based Feature Selection with a meta-heuristic algorithm (CFS-Meta), and the Heterogeneous Ensemble Model (HEM). The combined framework synergistically enhances the robustness and accuracy of SEE by effectively handling missing data, optimizing feature selection, and integrating diverse predictive models for superior performance across varying project scenarios. The framework significantly reduces imputation and feature selection overhead, while the ensemble approach optimizes model performance through dynamic weighting and meta-learning. This results in lower mean absolute error (MAE) and reduced computational complexity, making it more effective for diverse software datasets. NIVIM is engineered to address incomplete datasets prevalent in SEE. By integrating a synthetic data methodology through a Variational Auto-Encoder (VAE), the model incorporates both contextual relevance and intrinsic project features, significantly enhancing estimation precision. Comparative analyses reveal that NIVIM surpasses existing models such as VAE, GAIN, K-NN, and MICE, achieving statistically significant improvements across six benchmark datasets, with average RMSE improvements ranging from 11.05% to 17.72% and MAE improvements from 9.62% to 21.96%. Our proposed method, CFS-Meta, balances global optimization with local search techniques, substantially enhancing predictive capabilities. The proposed CFS-Meta model was compared to single and hybrid feature selection models to assess its efficiency, demonstrating up to a 25.61% reduction in MSE. Additionally, the proposed CFS-Meta achieves a 10% (MAE) improvement against the hybrid PSO-SA model, an 11.38% (MAE) improvement compared to the Hybrid ABC-SA model, and 12.42% and 12.703% (MAE) improvements compared to the hybrid Tabu-GA and hybrid ACO-COA models, respectively. Our third method proposes an ensemble effort estimation (EEE) model that amalgamates diverse standalone models through a Dynamic Weight Adjustment-stacked combination (DWSC) rule. Tested against international benchmarks and industry datasets, the HEM method has improved the standalone model by an average of 21.8% (Pred()) and the homogeneous ensemble model by 15% (Pred()). This comprehensive methodology underscores our model’s contributions to advancing software project management (SPM) through advanced predictive modeling, setting a new benchmark for software engineering effort estimation.

Multicenter cohort analysis of anoikis and EMT: implications for prognosis and therapy in lung adenocarcinoma

BackgroundAnoikis and epithelial-mesenchymal transition (EMT) are pivotal in the distant metastasis of lung adenocarcinoma (LUAD). A detailed understanding of their interplay and the identification of key genes is vital for effective therapeutic strategies against LUAD metastasis.MethodsKey prognostic genes related to anoikis and EMT were identified through univariate Cox regression analysis. We utilized ten machine learning algorithms to develop the Anoikis and EMT-Related Optimal Model (AEOM). The TCGA-LUAD dataset served as the training cohort, while six additional international multicenter LUAD datasets were employed as validation cohorts. The average concordance index (c-index) was used to evaluate model performance and identify the most effective model. Subsequent multi-omics analyses were conducted to explore differences in pathway enrichment, immune infiltration, and mutation landscapes between high and low AEOM groups. Experimental validation demonstrated that RHPN2, a key biomarker within the model, acts as an oncogene facilitating LUAD progression.ResultsThe AEOM displayed superior prognostic predictive performance for LUAD patients, outperforming numerous previously published LUAD signatures. Biologically, the AEOM was notably associated with immune features; the high AEOM group exhibited decreased immune activity and a tendency towards immune-cold tumors, as well as a higher tumor mutational burden (TMB). Subgroup analysis revealed that the low AEOM + high TMB group had the most favorable prognosis. The high AEOM group was primarily enriched in cell cycle-related pathways, promoting cancer cell proliferation. RHPN2, a crucial gene within the AEOM (correlation = 0.85, P < 0.05), was linked to poorer prognosis in LUAD patients with elevated RHPN2 expression. Further in vitro experiments showed that RHPN2 modulates LUAD cell proliferation and invasion.ConclusionThe AEOM provides a robust prognostic model for LUAD, uncovering critical immune and biological pathways, with RHPN2 identified as a key oncogenic driver. These findings offer valuable insights for targeted therapies and enhanced patient outcomes.

Beyond borders: The moderating role of cultural religiosity in the relationship between moral circle and generosity.

Research has indicated a link between the moral circle and an individual's prosocial behaviour. However, it remains uncertain whether this relationship holds across a broader range of countries and is influenced by sociocultural contexts. Drawing from an international dataset encompassing 49,540 participants from 57 countries, this study examined the impact of the moral circle on an individual's generosity and the moderating role of cultural religiosity. The results revealed a significant positive association between moral circle and generosity in a cross-national context. Notably, in religious cultures, the connection between the moral circle and generosity was found to be weaker. Three robustness checks further affirmed that these findings are robust. The research confirmed a positive relationship between the moral circle and generosity in a multinational context but also highlighted the moderating role of cultural religiosity. This finding suggested that future research should further investigate how cultural and religious frameworks influence ethical behaviour.

Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of -528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Board Responsibility for Irresponsibility: The Link Between Board Structure and Corporate Scandals

Based on an international data set that comprises over 6,100 companies located in 44 countries in the years 2002–2018, this paper analyzes the relation between corporate scandals and board structures besides further firm-related, political, nation-level economic, and cultural variables. We identify board structure variables that are positively associated with a firm’s corporate scandals, namely high CSR efforts and busy board members. There are also variables that are negatively associated with this kind of behavior, namely qualified and skilled boards. No clear evidence can be determined from a board’s gender diversity, independent board members, and board size.

Wait or pivot? Family and non-family firms’ strategic responses to COVID-19 and employment change

The aim of this paper is to investigate the effects of different strategic behaviors adopted during COVID-19 pandemic on employment change (a percentage change in the number of employees) in both family and non-family firms. We use an international survey-based dataset of 2,192 family and non-family firms. We provide evidence that a pivoting strategy (transforming the business) could prevent layoffs. Finally, while transparent leadership communication positively moderates the effect of pivoting on employment change in non-family firms it shows an opposite effect in family firms.

Does corporate sustainability performance matter for cash holdings? International evidence

PurposeWe examine the impact of corporate sustainability performance (CSP) on corporate cash holdings, focusing on the moderating impacts of industry’s concentration, financial constraints, and institutional environments.Design/methodology/approachThe empirical analysis is conducted on a sample of 31 countries from 2002 to 2018. We use the pooled OLS regressions controlling for fixed effects. We further address endogeneity issues using an instrumental variable approach, the Difference-in-Differences regression based on an exogenous shock, and the propensity score matching.FindingsWe find that firms with superior CSP hold more cash. This result is valid after a series of tests for robustness and endogeneity issues, suggesting a causal effect of CSP on corporate cash holdings. In the cross section, the positive impact of CSP on cash holdings is more pronounced for firms operating in highly concentrated industries, but attenuated for firms with financial constraints and for those operating in countries with better institutional environments. We further show that CSP affects cash holdings through the channel of financial distress risk.Practical implicationsIn making investment decisions, investors should not only examine corporate financial performance and sustainability profile, but also understand the related cash holding levels and financial distress costs. Corporate managers making decisions on levels of cash holdings should pay more attention to their sustainability behavior, especially for firms operating in concentrated industries and/or facing financial constraints. Governments and authorities can apply regulations to encourage firms to engage more in sustainable activities, as well as establish good institutional environments in the country.Originality/valueUsing a comprehensive international dataset, our paper contributes to two strands of literature: the economic impact of CSP and the driver of cash holdings. We further focus on the moderating role of industry concentration and firms’ financial constraints. Our international sample also allows us to exploit the effect of country-level informal institutions.

An international, open-access dataset of dental wear patterns and associated broad age classes in archaeological cattle mandibles

Zooarchaeologists investigate past interactions between animals, humans, and their environments by analyzing the remains of archaeological fauna. Age-at-death distributions are fundamental to faunal analysis and are often estimated by comparing exposed dentine patterns to standardized tooth wear stages that have been associated with relative age classes. We present Bubona, an international dataset of dental wear patterns and associated broad age classes in archaeological cattle mandibles. Our open-access dataset of 1460 data entries from nine counties is being used to create tooth-type specific reference tables of probable age class attribution for cattle mandibles lacking complete dentition. Bubona is a valuable resource for the innovation of new systems of age estimation for cattle and it is the creators hope that researchers will continue to both help expand the dataset by contributing their own data, as well as utilize the data to refine and innovate age-at-death estimation methods.

Structural Brain Differences in the Alzheimer’s Disease Continuum: Insights Into the Heterogeneity From a Large Multisite Neuroimaging Consortium

BackgroundNeurodegenerative diseases require collaborative, multisite research to comprehensively grasp their complex and diverse pathological progression; however, there is caution in aggregating global data due to data heterogeneity. In the current study, we investigated brain structure across stages of Alzheimer’s disease (AD) and how relationships vary across sources of heterogeneity. MethodsUsing 6 international datasets (n > 27,000), associations of structural neuroimaging markers were investigated in relation to the AD continuum via meta-analysis. We investigated whether associations varied across elements of magnetic resonance imagingacquisition, study design, and populations. ResultsModest differences in associations were found depending on how data were acquired; however, patterns were similar. Preliminary results suggested that neuroimaging marker–AD relationships differ across ethnic groups. ConclusionsDiversity in data offers unique insights into the neural substrate of AD; however, harmonized processing and transparency of data collection are needed. Global collaborations should embrace the inherent heterogeneity that exists in the data and quantify its contribution to research findings at the meta-analytical stage.

Student-centered teaching across OECD countries: An ecological perspective

Student-centered teaching (SCT) is recognized worldwide as a best-practice instructional method. This study, guided by Bronfenbrenner's ecological theory, investigates the influence of country, school, classroom, and teacher factors on SCT. Using a large international education dataset, we examine how the Human Development Index, Power Distance Index, and Individualism Index affect SCT at the national level. School dynamics like innovativeness, teacher cooperation, and teacher-student relationships are also considered, along with classroom autonomy and teachers' self-efficacy at the classroom- and teacher-level respectively. Combining advanced modeling techniques, the results show that power distance and individualism interact to influence SCT. Additionally, school innovativeness, teacher cooperation, and teacher self-efficacy significantly predict SCT at both school and teacher levels.

Follow the Leader: How Culture Gives Rise to a Behavioral Bias That Leads to Higher Greenhouse Gas Emissions

This research investigates the influence of national culture, particularly power distance, on firms’ carbon dioxide (CO2) emissions. Drawing on a large international dataset spanning over a decade, we examine how power distance, agency conflict, and socioeconomic stability interact to shape firms’ emission decisions. Our analysis reveals a significant positive relationship between power distance and firms’ CO2 emissions, indicating that firms located in countries characterized by higher power distance tend to emit more greenhouse gases (GHGs). Furthermore, we find that agency conflict moderates this relationship, with firms experiencing high levels of debt or paying substantial dividends exhibiting lower emissions in high power distance environments. Additionally, socioeconomic stability attenuates the positive association between power distance and emissions, suggesting that the effectiveness of cultural influences on emission decisions is contingent upon the stability of the societal context. These findings underscore the importance of considering cultural dimensions, agency dynamics, and socioeconomic conditions in understanding corporate environmental behavior. Our research contributes to the literature by providing empirical evidence of the nuanced interplay between national culture, agency conflict, and socioeconomic stability in shaping firms’ emission decisions. Policymakers and practitioners can use these insights to develop more targeted environmental policies and strategies aimed at promoting sustainable development globally.

Is corporate community involvement associated with poverty and income inequality? International evidence

ABSTRACT This study explores the relationship between economic conditions and corporate community involvement (CCI) across an international dataset of 7,926 firm-year observations from 33 countries during 2013–2019. We investigate whether and how poverty levels and income inequality within a country are associated with firms’ engagement in social welfare initiatives. Our findings suggest that firms are more active in community contributions in the presence of severe poverty and greater income inequality. Specifically, in developed countries, both factors are strongly linked to CCI, while in developing countries, CCI primarily correlates with income inequality. This research contributes to the literature by showing that firms adjust their ESG strategies based on country-specific economic conditions.