Month: September 2024

Global analysis of social learning’s archetypes in natural resource management: understanding pathways of co-creation of knowledge

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Abstract

Although social learning (SL) conceptualization and implementation are flourishing in sustainability sciences, and its non-rigid conceptual fluidity is regarded as an advantage, research must advance the understanding of SL phenomenon patterns based on empirical data, thus contributing to the identification of its forms and triggering mechanisms, particularly those that can address urgent Anthropocene socio-ecological problems. This study aims to discover fundamental patterns along which SL in natural resources management differs by identifying SL archetypes and establishing correlations between the SL process and overall geopolitical conditions. Using a systematic literature review comprising 137 case studies in the five continents, content analysis, and correlations were performed. Results show two main archetypes of social learning (endogenous and exogenous). Their occurrence was linked, to where social learning occurs and how venues/preconditions for social learning are placed. In the Global South, endogenous SL should be better potentialized as a catalyzer of deliberative processes for sustainable natural resources management.

Place identity: a generative AI’s perspective

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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

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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.

A review of geospatial exposure models and approaches for health data integration

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Abstract

Background

Geospatial methods are common in environmental exposure assessments and increasingly integrated with health data to generate comprehensive models of environmental impacts on public health.

Objective

Our objective is to review geospatial exposure models and approaches for health data integration in environmental health applications.

Methods

We conduct a literature review and synthesis.

Results

First, we discuss key concepts and terminology for geospatial exposure data and models. Second, we provide an overview of workflows in geospatial exposure model development and health data integration. Third, we review modeling approaches, including proximity-based, statistical, and mechanistic approaches, across diverse exposure types, such as air quality, water quality, climate, and socioeconomic factors. For each model type, we provide descriptions, general equations, and example applications for environmental exposure assessment. Fourth, we discuss the approaches used to integrate geospatial exposure data and health data, such as methods to link data sources with disparate spatial and temporal scales. Fifth, we describe the landscape of open-source tools supporting these workflows.

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

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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

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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

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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.