Author: Latest Results

Place identity: a generative AI’s perspective

Latest Results

Abstract

Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies.

Negative news headlines are more attractive: negativity bias in online news reading and sharing

Latest Results

Abstract

Clickbait—online content designed to attract attention and clicks through misleading or exaggerated headlines—has become a prevalent phenomenon in online news. Previous research has sparked debate over the effectiveness of clickbait strategies and whether a bias toward negativity or positivity drives online news engagement. To clarify these issues, we conducted two studies. Study 1 examined participants’ preferences for news headlines, revealing a higher selection rate for negative headlines. This finding indicates a negativity bias in the news reading process and underscores the effectiveness of negative information in clickbait strategies. Study 2 simulated the process of news sharing and examined how participants generalize and report negative news. The findings show that participants amplified the negativity of the original news by using more negative terms or introducing new negative language, demonstrating an even stronger negativity bias during news sharing. These findings affirm the presence of a negativity bias in online engagement, in reading and sharing news. This study offers psychological insights into the clickbait phenomenon and provides theoretical support and practical implications for future research on negativity bias in online news.

Ensemble characteristics of an analog ensemble (AE) system for simultaneous prediction of multiple surface meteorological variables at local scale

Latest Results

Abstract

Ensemble characteristics of a 10-member analog ensemble (AE) system for simultaneous prediction of six surface meteorological variables are examined at six station locations in the north-west Himalaya (NWH), India for lead times, 0 h (0 h)[d0], 24 h (d1), 48 h (d2) and 72 h (d3). The maximum (MMX), minimum (MNX) and mean (ME) values of each variable in analog days are found to exhibit statistically significant positive correlations with their corresponding observations at each station location for d0 through d3. The MEs of the variables are found to reproduce statistics (temporal mean, temporal standard deviation), empirical distributions of the observations on the variables reasonably well, and the MEs of the variables exhibit reasonable values of the RMSEs for d0 through d3. The observations on each variable and multiple variables simultaneously fall within their ranges (MMXs, MNXs) in ensemble members for maximum number of days for all lead times. The AE system is found to exhibit high spatial and temporal consistency in its predictive characteristics at six station locations in the NWH. Despite our short length data, these results are very interesting and suggest practical utility of the AE system for simultaneous prediction of variables at local scale utilizing local scale surface meteorological observations. Similar studies on various other types of ensemble systems can help to assess their practical utility for various forecasting applications.

Unequally Happy: Happiness Inequality Across Satisfaction Domains in a Developing-Country Context

Latest Results

Abstract

Subjective measures of well-being, such as happiness, occupy a rapidly growing body in the academic literature. However, how happiness levels are distributed across populations and social groups is less well known, especially in the context of developing countries. In this paper, I study happiness inequality at the district level in Ecuador, considering diverse domains of individual satisfaction. Concretely, I calculate Gini coefficients for happiness domains and identify the determinants of happiness inequality levels using a panel dataset for 584 districts over three years. The findings show that happiness inequality is lower regarding overall satisfaction and satisfaction with marital status and social life and higher regarding work and financial conditions, and the government. The results also suggest that average happiness level and income inequality are consistent determinants of happiness inequality. Lastly, I divide the sample by gender, place of residence, ethnicity and education levels to explore the differences across population groups. Policy discussion and implications follow the quantitative analysis.

Competency in invasion science: addressing stagnation challenges by promoting innovation and creative thinking

Latest Results

Abstract

In today’s ever-evolving scientific landscape, invasion science faces a plethora of challenges, such as terminological inconsistency and the rapidly growing literature corpus with few or incomplete syntheses of knowledge, which may be perceived as a stagnation in scientific progress. We explore the concept of ‘competency’, which is extensively debated across disciplines such as psychology, philosophy, and linguistics. Traditionally, it is associated with attributes that enable superior performance and continuous ingenuity. We propose that the concept of competency can be applied to invasion science as the ability to creatively and critically engage with global challenges. For example, competency may help develop innovative strategies for understanding and managing the multifaceted, unprecedented challenges posed by the spread and impacts of non-native species, as well as identifying novel avenues of inquiry for management. Despite notable advancements and the exponential increase in scholarly publications, invasion science still encounters obstacles such as insufficient interdisciplinary collaboration paralleled by a lack of groundbreaking or actionable scientific advancements. To enhance competency in invasion science, a paradigm shift is needed. This shift entails fostering interdisciplinary collaboration, nurturing creative and critical thinking, and establishing a stable and supportive environment for early career researchers, thereby promoting the emergence of competency and innovation. Embracing perspectives from practitioners and decision makers, alongside diverse disciplines beyond traditional ecological frameworks, can further add novel insights and innovative methodologies into invasion science. Invasion science must also address the ethical implications of its practices and engage the public in awareness and education programs. Such initiatives can encourage a more holistic understanding of invasions, attracting and cultivating competent minds capable of thinking beyond conventional paradigms and contributing to the advancement of the field in a rapidly changing world.

Investigating the role of source and source trust in prebunks and debunks of misinformation in online experiments across four EU countries

Latest Results

Abstract

Misinformation surrounding crises poses a significant challenge for public institutions. Understanding the relative effectiveness of different types of interventions to counter misinformation, and which segments of the population are most and least receptive to them, is crucial. We conducted a preregistered online experiment involving 5228 participants from Germany, Greece, Ireland, and Poland. Participants were exposed to misinformation on climate change or COVID-19. In addition, they were pre-emptively exposed to a prebunk, warning them of commonly used misleading strategies, before encountering the misinformation, or were exposed to a debunking intervention afterwards. The source of the intervention (i.e. the European Commission) was either revealed or not. The findings show that both interventions change four variables reflecting vulnerability to misinformation in the expected direction in almost all cases, with debunks being slightly more effective than prebunks. Revealing the source of the interventions did not significantly impact their overall effectiveness. One case of undesirable effect heterogeneity was observed: debunks with revealed sources were less effective in decreasing the credibility of misinformation for people with low levels of trust in the European Union (as elicited in a post-experimental questionnaire). While our results mostly suggest that the European Commission, and possibly other public institutions, can confidently debunk and prebunk misinformation regardless of the trust level of the recipients, further evidence on this is needed.

Online Prosumers and Penal Policy Formation in an Age of Digital Polarization and Populism: An Exploratory Study

Latest Results

Abstract

This article explores the influence of right-wing social media users on penal policy formation processes. Through a passive digital ethnography approach, the study examines online debates preceding and following recent legislative interventions adopted in Italy by the new right-wing government in power since late 2022, namely the criminalization of unauthorized rave parties and the punitive approach to migration management. The article discusses the role of social media users as prosumers, who both consume and produce content, and shows how social media platforms amplify political polarization by promoting selective exposure to like-minded viewpoints and facilitating the spread of divisive content. It also showcases how prosumers contribute to the propagation of punitive narratives and engage in direct interactions with populist leaders through social media platforms. Conversely, political leaders—specifically Italy’s Prime Minister Giorgia Meloni in this case study—use these platforms to disseminate their narratives and create support for their penal policies, employing fear-mongering tactics and simplistic messaging. Our findings suggest that, while social media platforms have transformed political discourse, in the Italian scenario their direct influence on penal policy making from the ground-up remains limited. Instead, traditional top-down channels continue to dominate the process of penal policy formation.

Knowledge, attitude and practice levels regarding malaria among the Semai sub-ethnic indigenous Orang Asli communities in Pahang, Peninsular Malaysia: a stepping stone towards the prevention of human malaria re-establishment

Latest Results

Abstract

Background

In Malaysia, despite a decline in cases, malaria remains a major public health concern, especially among the vulnerable indigenous people (i.e. Orang Asli) in remote areas. Effective preventive and control measures require an evidence-based understanding of their knowledge, attitudes, and practices (KAP) regarding malaria. This study aimed to evaluate the KAP regarding malaria in an indigenous settlement in Peninsular Malaysia.

Methods

A household-based cross-sectional study was conducted in March 2024 in six Semai sub-ethnic indigenous villages in Pos Lenjang, Kuala Lipis, Pahang. A structured questionnaire was administered to randomly selected individuals (≥ 12 years old) to collect data on sociodemographic characteristics and KAP. Data were analysed using descriptive statistics and predictors of KAP were determined using logistic regression. A p-value less than 0.05 was considered statistically significant.

Results

A total of 267 individuals from 160 households were interviewed. Nearly half had good knowledge (49.4%) and positive attitudes (54.3%) towards malaria, with high practice scores for prevention and control (83.1%). Multivariate logistic regression analysis showed higher odds of good knowledge in those aged 40–59 years (adjusted odd ratio [aOR] = 6.90, p = 0.034), with primary (aOR = 2.67, p = 0.015) or secondary education (aOR = 2.75, p = 0.019), and with previous malaria history (aOR = 5.14, p < 0.001). Higher odds of a good attitude were found in those with secondary education (aOR = 4.05, p < 0.001) and previous malaria history (aOR = 2.74, p = 0.017). Lower odds were observed for the unemployed (aOR = 0.25, p = 0.018) and those collecting forest products (aOR = 0.25, p = 0.049) for attitude and practice, respectively.

Discussion

The overall practice level on malaria prevention was high among the Semai Orang Asli in Pahang. However, to ensure sustainability, the low levels of knowledge and attitude regarding malaria must be strengthened through increased health education and continuous community engagement.

Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

Latest Results

Abstract

Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes.

Provenance through storytelling: application of Indigenous relationality toward arrangement and description

Latest Results

Abstract

Every culture creates and keeps records. Archivists have a pivotal responsibility toward the relationality of the historical past to present societal structure to preserve records of evidential and historical value and ensure their accessibility. Despite cultural differences, archivists impose colonial theory onto Indigenous archival materials that result in a lack of context. Because provenance is a colonial construct, it is often challenged when applied to cultural materials. In this article, the principle of provenance is discussed and challenged, against the backdrop of Indigenous archival practice that centers relationality and reciprocity in stewardship. Highlighting the example of the Jean Chaudhuri Collection at the Arizona State University Labriola National American Data Center, archivists employed a storytelling provenance providing rich context and description about the impactful life of Indigenous activist, Jean Chaudhuri. By reimagining and employing a practical, alternative provenance method, the principle of provenance, expands to respectfully support and provide context that was lacking, resulting in improved accessibility to a collection.