Cologna et al. (2025) compared perceived scientist trustworthiness across 68 countries/regions and examined its associations with individual- and country-level factors. While the authors reported that the scale did not satisfy metric and scalar measurement invariance, their subsequent cross-national/regional comparisons and regressions were nonetheless conducted using weighted means of observed item scores, implicitly assuming cross-country/region comparability at the observed-score level. Using the publicly shared dataset, we re-evaluated these conclusions by systematically applying measurement alignment under four analytical paths: pooled-sample versus country/region-specific confirmatory factor analysis (CFA), each estimated with and without weights. Across all specifications, cross-national/regional CFA supported configural and metric invariance but failed to establish scalar or strict invariance. Importantly, under the analytical path most closely corresponding to the original study (pooled CFA with weights), only the competence and openness factors yielded admissible aligned solutions within the four-factor model. Using aligned latent scores for these two dimensions, country/region rankings changed for 62 of the 68 countries/regions. Substantive conclusions also differed: associations between perceived scientist trustworthiness and science-related populist attitudes or social dominance orientation were near zero or non-robust, whereas attitudes toward science remained strongly and positively related. Taken together, these findings demonstrate that cross-national/regional comparisons based on observed-score averages may be misleading when measurement equivalence is not established, and that latent-variable approaches such as alignment provide a more defensible basis for international inference.
Tuhin G. M. Al Mamun, Ehsanullah, Md. Sharif Hassan
et al.
Rising CO$_2$ emissions remain a critical global challenge, particularly in middle-income countries where economic growth drives environmental degradation. This study examines the long-run and short-run relationships between CO$_2$ emissions, energy use, GDP per capita, and population across 106 middle-income countries from 1980 to 2023. Using a Panel Vector Error Correction Model (VECM), we assess the impact of the Paris Agreement (2015) on emissions while conducting cointegration tests to confirm long-run equilibrium relationships. The findings reveal a strong long-run relationship among the variables, with energy use as the dominant driver of emissions, while GDP per capita has a moderate impact. However, the Paris Agreement has not significantly altered emissions trends in middle-income economies. Granger causality tests indicate that energy use strongly causes emissions, but GDP per capita and population do not exhibit significant short-run causal effects. Variance decomposition confirms that energy shocks have the most persistent effects, and impulse response functions (IRFs) show emissions trajectories are primarily shaped by economic activity rather than climate agreements. Robustness checks, including autocorrelation tests, polynomial root stability, and Yamagata-Pesaran slope homogeneity tests, validate model consistency. These results suggest that while global agreements set emissions reduction goals, their effectiveness remains limited without stronger national climate policies, sectoral energy reforms, and financial incentives for clean energy adoption to ensure sustainable economic growth.
Employers increasingly expect graduates to utilize large language models (LLMs) in the workplace, yet the competencies needed for computing roles across Africa remain unclear given varying national contexts. This study examined how six LLMs, namely ChatGPT 4, DeepSeek, Gemini, Claude 3.5, Llama 3, and Mistral AI, describe entry-level computing career expectations across ten African countries. Using the Computing Curricula 2020 framework and drawing on Digital Colonialism Theory and Ubuntu Philosophy, content analysis of 60 LLM responses to standardized prompts reveals consistent coverage of technical competencies such as cloud computing and programming, but notable differences in non-technical competencies, particularly ethics and responsible AI use. Models vary considerably in recognizing country-specific factors, including local technology ecosystems, language requirements, and national policies averaging only 35.4% contextual awareness overall. Open-source models demonstrated stronger contextual awareness and better balance between technical and professional skills, with Llama (4.47/5) and DeepSeek (4.25/5) outperforming proprietary alternatives ChatGPT-4 (3.90/5) and Claude (3.46/5). However, Mistral's poor contextual performance (0.00/4) despite being open-source indicates that development philosophy alone does not guarantee contextual responsiveness. This first comprehensive comparison of LLM career guidance for African computing students uncovers entrenched infrastructure assumptions and Western-centric biases that create gaps between technical recommendations and local realities. The findings challenge assumptions about AI tool quality in resource-constrained settings and underscore the need for decolonial approaches to AI in education, emphasizing contextual relevance and hybrid human-AI guidance models.
This study investigates the impact of Foreign Direct Investment (FDI) on economic growth in South Asian countries, utilizing annual panel data from five SAARC member states (Bangladesh, India, Nepal, Pakistan, and Sri Lanka) over the period 1980-2017. Data sourced from the World Development Indicators and Penn World Table were analyzed using static panel models, including Ordinary Least Squares, Fixed Effects, Random Effects, and Generalized Least Squares regressions. The empirical findings reveal that FDI exhibits a consistently positive but statistically insignificant correlation with economic growth across all model specifications. In contrast, domestic investment and human capital development emerge as significant and robust positive determinants of growth. Control variables such as government consumption and inflation show expected negative, though generally insignificant, associations with growth. The results imply that for the sampled South Asian economies, enhancing domestic investment and fostering human capital are more critical for driving economic expansion than relying on FDI inflows. Consequently, policymakers should prioritize strategies that strengthen local investment climates and improve educational and skill-building institutions to boost productivity. While FDI's role remains complementary, its insignificant immediate impact suggests the need for further research into the conditional factors such as institutional quality, financial market development, and trade policies that might mediate its effectiveness in fostering long-term growth within the region.
Some research now suggests that ChatGPT can estimate the quality of journal articles from their titles and abstracts. This has created the possibility to use ChatGPT quality scores, perhaps alongside citation-based formulae, to support peer review for research evaluation. Nevertheless, ChatGPT's internal processes are effectively opaque, despite it writing a report to support its scores, and its biases are unknown. This article investigates whether publication date and field are biasing factors. Based on submitting a monodisciplinary journal-balanced set of 117,650 articles from 26 fields published in the years 2003, 2008, 2013, 2018 and 2023 to ChatGPT 4o-mini, the results show that average scores increased over time, and this was not due to author nationality or title and abstract length changes. The results also varied substantially between fields, and first author countries. In addition, articles with longer abstracts tended to receive higher scores, but plausibly due to such articles tending to be better rather than due to ChatGPT analysing more text. Thus, for the most accurate research quality evaluation results from ChatGPT, it is important to normalise ChatGPT scores for field and year and check for anomalies caused by sets of articles with short abstracts.
We investigate the presence of sign and size non-linearities in the impact of the European Central Bank$^\prime$s Anti-Fragmentation Policy on non-ERM II, EU countries. After identifying three orthogonal monetary policy shock using the method of Fanelli and Marsi [2022], we then select an optimal specification and estimate both linear and non linear impulse response functions using local projections (Dufour and Renault [1998], Goncalves et al. [2021]). The choice of non-linear transformations to separate sign and size effects is based on Caravello and Martinez-Bruera [Working Paper, 2024]. Lastly we compare the linear model to the non-linear ones using a battery of Wald tests and find significant evidence of sign non-linearities in the international spillovers of ECB policy.
As the internet penetration rate in Africa increases, so does the proliferation of the Internet of Things (IoT) devices. Along with this growth in internet access is the risk of cyberattacks to vulnerable IoT devices mushrooming in the African cyberspace. One way to determine IoT vulnerabilities is to find open ports within Africa s cyberspace. Our research leverages Shodan search engine, a powerful tool for discovering IoT devices facing the public internet, to find open ports across Africa. We conduct an analysis of our findings, ranking countries from most to least vulnerable to cyberattack. We find that South Africa,Tunisia, Morocco, Egypt, and Nigeria are the five countries most susceptible to cyberattack on the continent. Further, 69.8% of devices having one of the five most commonly open internet ports have had past documented vulnerabilities. Following our analysis, we conclude with policy recommendations for both the public and private sector.
Lara Herriott, Henriette L. Capel, Isaac Ellmen
et al.
Mathematical models play a crucial role in understanding the spread of infectious disease outbreaks and influencing policy decisions. These models aid pandemic preparedness by predicting outcomes under hypothetical scenarios and identifying weaknesses in existing frameworks. However, their accuracy, utility, and comparability are being scrutinized. Agent-based models (ABMs) have emerged as a valuable tool, capturing population heterogeneity and spatial effects, particularly when assessing intervention strategies. Here we present EpiGeoPop, a user-friendly tool for rapidly preparing spatially accurate population configurations of entire countries. EpiGeoPop helps to address the problem of complex and time-consuming model set up in ABMs, specifically improving the integration of spatial detail. We subsequently demonstrate the importance of accurate spatial detail in ABM simulations of disease outbreaks using Epiabm, an ABM based on Imperial College London's CovidSim with improved modularity, documentation and testing. Our investigation involves the interplay between population density, the implementation of spatial transmission, and realistic interventions implemented in Epiabm.
Sierra Pugh, Andrew T. Levin, Gideon Meyerowitz-Katz
et al.
The COVID-19 infection fatality rate (IFR) is the proportion of individuals infected with SARS-CoV-2 who subsequently die. As COVID-19 disproportionately affects older individuals, age-specific IFR estimates are imperative to facilitate comparisons of the impact of COVID-19 between locations and prioritize distribution of scare resources. However, there lacks a coherent method to synthesize available data to create estimates of IFR and seroprevalence that vary continuously with age and adequately reflect uncertainties inherent in the underlying data. In this paper we introduce a novel Bayesian hierarchical model to estimate IFR as a continuous function of age that acknowledges heterogeneity in population age structure across locations and accounts for uncertainty in the estimates due to seroprevalence sampling variability and the imperfect serology test assays. Our approach simultaneously models test assay characteristic, serology, and death data, where the serology and death data are often available only for binned age groups. Information is shared across locations through hierarchical modeling to improve estimation of the parameters with limited data. Modeling data from 26 developing country locations during the first year of the COVID-19 pandemic, we found seroprevalence did not change dramatically with age, and the IFR at age 60 was above the high-income country benchmark for most locations.
Thushari Atapattu, Mahen Herath, Charitha Elvitigala
et al.
People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person's choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent), consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including 'mental illness' (e.g., depression) and emotions (e.g., 'sadness', 'anger'). EmoMent corpus achieved 'very good' inter-annotator agreement of 98.3% (i.e. % with two or more agreement) and Fleiss' Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.
To the Editor, The emergence of severe acute respiratory syndrome 2 (SARS‐CoV‐ 2) variants of concern (VOC) have posed a serious threat to the control of the disease worldwide. Of note are the B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma) that were first reported from the United Kingdom, South Africa, and Brazil, respectively. The B.1.351 contains characteristic mutations in the receptor‐binding domain of spike glycoprotein (S) protein: K417N, E484K, and N501Y that have functional significance. As of May 7, 2021, 14 543 sequences of B.1.351 lineage have been reported from 85 countries around the world. Since the start of the pandemic, the National Institute of Health, Pakistan has been involved in the surveillance of SARS‐CoV‐2 and although the B.1.1.7 cases have been detected, the whole‐ genome sequence of B.1.351 has not been reported in Pakistan. We hereby report the genomic diversity of the first two sequences of the B.1.351 variant detected from Pakistan. On April 24 and 26, 2021, two oropharyngeal samples were received at the Department of Virology, National Institute of Health for the detection of SARS‐CoV‐2 as part of routine surveillance. Briefly, viral RNA was extracted using the MagMAXTM Viral/Pathogen Nucleic Acid Isolation Kit on the KingFisher FlexTM Purification System (Thermo Fisher Scientific). The TaqPathTM COVID‐19 CE‐IVD RT‐PCR kit (Thermo Fisher Scientific) was used for the detection of SARS‐CoV‐2. These two samples were selected for whole‐genome sequencing due to amplification of spike gene onTaqPathTM assay and having low Ct value (<22 for S gene). For whole‐genome sequencing, the cDNA synthesis and amplification were performed according to the Primal‐Seq Nextera XT protocol (version 2) using SuperScriptTM IV VILOTM Master Mix (Invitrogen) and Q5 High‐Fidelity 2X Master Mix (New England BioLabs) respectively, with the ARTIC nCoV‐2019 Panel (Integrated DNA Technologies, Inc.). Illumina DNA Prep Kit (Illumina, Inc.) was used for library preparation and subjected to sequencing on Illumina iSeq 100 (Illumina, Inc.). The read quality of sequenced files was assessed using the FastQC tool (v0.11.9). The data were processed and analyzed as per recommended guidelines of the Centers for Diseases Control and Prevention. On real‐time PCR, the sample NIH‐S6 (EPI_ISL_1969999) showed a Ct value of 24 for N‐gene, 23 for ORF1ab, and 21 for S‐gene. The patient was a 28‐year‐old female who attended a funeral ceremony in her neighborhood on April 20, 2021, before infection. The patient had mild symptoms of having body aches, loss of taste and smell, and had no travel history. Similarly, the Ct values of sample NIH‐S12 (EPI_ISL_1969995) were 14 for N‐gene, 16 for ORF1ab, and 14 for S‐gene. The patient was a 30‐year‐old male with low‐grade fever and body aches and had no travel history. Whole‐genome sequencing results showed the detection of B.1.351 in both cases having characteristic mutations in the spike: D80A, D215G, D614G, E484K, K417N, N501Y, and A701V (Table 1). Based on phylogenetic analysis, Pakistani sequences were closely related to the B.1.351 (Figure 1). As Pakistan fights against the COVID‐19 pandemic, a total of 864 557 cases and 19 106 deaths have been reported until May 10, 2021, with a high number of cases (n = 283 192) seen during the third wave (https://covid.gov.pk/stats/pakistan). Based on our laboratory data (spike gene target failure cases using COVID‐19 TaqPathTM kit; Thermo Fisher Scientific), the recent surge in cases correlates with the detection of the B.1.1.7 variant in the country. Conversely, no data is available on the prevalence of other VOCs including B.1.351,
Recent event of ousting Rohingyas from Rakhine State by the Tatmadaw provoked worldwide public-and-academic interest in history and social evolution of the Rohingyas, and this is to what the article is devoted. As the existing literature presents a debate over Who are the Rohingyas?, and How legitimate is their claim over Rakhine State?, the paper reinvestigates the issues using a qualitative research method. Compiling a detailed history, the paper finds that Rohingya community developed through historically complicated processes marked by invasions and counter-invasions. The paper argues many people entered Bengal from Arakan before British brought people into Rakhine state. The Rohingyas believe Rakhine State is their ancestral homeland and they developed a sense of Ethnic Nationalism. Their right over Rakhine State is as significant as other groups. The paper concludes that the UN must pursue solution to the crisis and the government should accept the Rohingyas as it did the land or territory.
The ongoing COVID-19 pandemic has profoundly impacted people's life around the world, including how they interact with mobile technologies. In this paper, we seek to develop an understanding of how the dynamic trajectory of a pandemic shapes mobile phone users' experiences. Through the lens of app popularity, we approach this goal from a cross-country perspective. We compile a dataset consisting of six-month daily snapshots of the most popular apps in the iOS App Store in China and the US, where the pandemic has exhibited distinct trajectories. Using this longitudinal dataset, our analysis provides detailed patterns of app ranking during the pandemic at both category and individual app levels. We reveal that app categories' rankings are correlated with the pandemic, contingent upon country-specific development trajectories. Our work offers rich insights into how the COVID-19, a typical global public health crisis, has influence people's day-to-day interaction with the Internet and mobile technologies.
Tuberculosis (TB) is a top-10 cause of death worldwide. Though the WHO recommends chest radiographs (CXRs) for TB screening, the limited availability of CXR interpretation is a barrier. We trained a deep learning system (DLS) to detect active pulmonary TB using CXRs from 9 countries across Africa, Asia, and Europe, and utilized large-scale CXR pretraining, attention pooling, and noisy student semi-supervised learning. Evaluation was on (1) a combined test set spanning China, India, US, and Zambia, and (2) an independent mining population in South Africa. Given WHO targets of 90% sensitivity and 70% specificity, the DLS's operating point was prespecified to favor sensitivity over specificity. On the combined test set, the DLS's ROC curve was above all 9 India-based radiologists, with an AUC of 0.90 (95%CI 0.87-0.92). The DLS's sensitivity (88%) was higher than the India-based radiologists (75% mean sensitivity), p<0.001 for superiority; and its specificity (79%) was non-inferior to the radiologists (84% mean specificity), p=0.004. Similar trends were observed within HIV positive and sputum smear positive sub-groups, and in the South Africa test set. We found that 5 US-based radiologists (where TB isn't endemic) were more sensitive and less specific than the India-based radiologists (where TB is endemic). The DLS also remained non-inferior to the US-based radiologists. In simulations, using the DLS as a prioritization tool for confirmatory testing reduced the cost per positive case detected by 40-80% compared to using confirmatory testing alone. To conclude, our DLS generalized to 5 countries, and merits prospective evaluation to assist cost-effective screening efforts in radiologist-limited settings. Operating point flexibility may permit customization of the DLS to account for site-specific factors such as TB prevalence, demographics, clinical resources, and customary practice patterns.
M. Jahangir Alam, Benoit Dostie, Jörg Drechsler
et al.
Data on businesses collected by statistical agencies are challenging to protect. Many businesses have unique characteristics, and distributions of employment, sales, and profits are highly skewed. Attackers wishing to conduct identification attacks often have access to much more information than for any individual. As a consequence, most disclosure avoidance mechanisms fail to strike an acceptable balance between usefulness and confidentiality protection. Detailed aggregate statistics by geography or detailed industry classes are rare, public-use microdata on businesses are virtually inexistant, and access to confidential microdata can be burdensome. Synthetic microdata have been proposed as a secure mechanism to publish microdata, as part of a broader discussion of how to provide broader access to such data sets to researchers. In this article, we document an experiment to create analytically valid synthetic data, using the exact same model and methods previously employed for the United States, for data from two different countries: Canada (LEAP) and Germany (BHP). We assess utility and protection, and provide an assessment of the feasibility of extending such an approach in a cost-effective way to other data.