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Monumentally Kitsch: The Decommissioned Captain Cook Statues of Aotearoa (New Zealand) and Australia
No full textThe onset of the COVID-19 pandemic and global reignition of the Black Lives Matter movement in 2020 reorientated activists, the media, and scholars worldwide toward the meanings associated with colonial statutory. In Aotearoa (New Zealand) and Australia, this reorientation coincided with the 250th anniversary of navigator Captain James Cook’s first Pacific voyage. The number of Cook monuments in these settler-colonial nations evinces that Cook is an historical figure with an outsized legacy. This article examines the histories and fates of two particularly unusual Cook statues, one in Tūranga (Gisborne), Aotearoa, and one in Cairns, Australia. Amid so many Cook monuments, why have these two statues alone been taken down? This article argues that statues celebrating colonial figures can be seen as falling within the genre of kitsch, but that these two statues are extreme examples of kitsch aesthetics. Their obvious embodiment of kitsch and provocation of mirth in viewers proved pivotal in the decommissioning of these antipodean statues in 2019 and 2022. The fates of the statues called “Crook Cook” and “Nazi Captain Cook” analyzed in this article indicate that the aesthetics of colonial statues can be as significant a factor in their removal as the historical behavior of their subjects
Traditional practices versus modern healthcare: Determinants of traditional medicine use after potential dog bites among dog-owning households in Nigeria
No full textCanine rabies is endemic in Nigeria, with a low dog vaccination rate. Often, dog bite victims resort to traditional remedies, which can lead to fatalities. Our study investigated factors influencing decisions to seek traditional remedies in Nigeria. We conducted a cross-sectional study in 2022 involving 4,162 dog-owning households. A joint random effect Bayesian regression model was developed to examine the role of sociodemographic, socioeconomic, and infrastructural covariates. This model included a latent variable measuring a respondent’s understanding of rabies risk based on literacy levels and responses to questions about rabies epidemiology. Our results indicated that 27% (95% Confidence Interval [Cl); 26-27) of respondents would preferably seek traditional remedies following a dog bite. Male respondents were 24% more likely than female respondents to seek traditional remedies (odds ratio [OR]: 1.24; 95%, Credible Interval CrI): 1.07-1.31). Similarly, individuals residing in rural areas reported 55% higher likelihood of using traditional remedies than those in urban areas (OR: 1.55; 95% CrI: 1.43–1.67). Respondents residing in areas with no veterinary services reported 35% higher likelihood of using traditional remedies than those near such facilities (OR: 1.35; 95% CrI: 1.15–1.42). Children under 16 years reported 27% lower likelihood of using traditional remedies than adults (OR: 0.73; 95% CrI: 0.49–0.84). Private or unemployed individuals were more likely to seek traditional remedies than civil servants (OR: 1.99; 95% Crl: 1.53-2.37). Respondents with tertiary education reported 42% lower likelihood of using traditional remedies than those without formal education (OR: 0.58; 95% CrI: 0.49–0.62). Our latent variable representing understanding of rabies risk was negatively associated with the probability of seeking traditional remedies (OR: 0.67; 95% CrI: 0.54–0.73). Lastly, poverty was negatively associated with the likelihood of seeking traditional remedies (OR: 0.78; 95% CrI: 0.66–0.82). Our findings provide important insights into healthcare behaviour decisions and their possible associations with rabies outcomes in Nigeria. These results highlight the need to improve public education, enhance access to medical care, and involve traditional healers in rabies prevention and control program
Managing Gender Equity and Equality Across Borders
No full textAchieving gender equality remains a pressing global challenge. In response, many organizations and multinational enterprises (MNEs) have adopted gender diversity management (GDM)—human resource practices aimed at promoting gender equity and equality in the workplace. While prior research highlights the importance of institutional context in shaping the implementation and outcomes of GDM, there is limited understanding of how to contextualize and implement these practices effectively across diverse national settings. In this this editorial, we first review existing research in three key areas: (1) the transfer of GDM practices across MNEs, (2) the gender composition of MNEs’ top management teams, and (3) comparative studies of GDM. Our analysis underscores the limitations of universal, “one‐size‐fits‐all” approaches and emphasizes the need for context‐sensitivity. In this context, we then introduce the contributions to the Special Issue. Together, these articles advance our understanding of the complex interplay between organizational practices and local norms in shaping GDM implementation and outcomes. Finally, we outline research directions that can help propel future work, including the need for a deeper understanding of MNEs’ motivations for engaging in GDM, the positioning of gender within broader diversity agendas, and the implications of growing anti‐DEI sentiment
Environmentally Sustainable Tourist Behavior in ASEAN
No full textNurhafihz Noor, Zahirah Zainol, and Ashley Tong conduct a systematic literature review to identify key factors influencing environmentally sustainable tourist behavior in the ASEAN region. They categorize these factors into six areas—service, tourist, destination, social, stakeholder, and technology—and apply the Antecedents, Decisions, and Outcomes (ADO) framework to better understand the motivations driving responsible tourism behavior in the region. Based on their findings, the chapter offers three key initiatives: (1) promoting education and awareness campaigns to encourage environmentally sustainable behaviors, with a special focus on eco-friendly cultural practices; (2) encouraging the adoption of sustainable certifications and labels by businesses; and (3) advocating for revenue-sharing models, where a portion of proceeds supports local communities, with businesses being recognized and rewarded for their green achievements
The structure and composition of macroalgal communities influence coral recruitment on an inshore reef of the Great Barrier Reef
No full textOn inshore coral reefs, coral cover declines from disturbances are often accompanied by increases in macroalgal cover. Thus, coral recovery often occurs against a backdrop of elevated macroalgae cover. While ‘macroalgae’ are generally assumed to reduce coral recruitment, their taxonomic composition and structure vary considerably. Here, we test whether different macroalgal assemblages affect coral recruitment on an inshore reef by experimentally manipulating macroalgal assemblages within forty 1 m2 plots on the shallow reef crest in Florence Bay, Magnetic Island (central inshore Great Barrier Reef). Specifically, we investigated the effect of canopy-forming macroalgae (e.g. Sargassum, Turbinaria, Sirophysalis), understorey macroalgae (e.g. Hypnea, Lobophora, Padina), mixed macroalgal assemblages (both canopy- and understorey macroalgae) and plots cleared of macroalgae on rates of coral recruitment to tiles. We also quantified coral size frequency distribution in Florence Bay to investigate its relationship with macroalgal structure and composition. The presence of canopy-forming macroalgae was the most important factor affecting coral recruitment, with coral recruitment being ~ fivefold greater in plots with no canopy-forming macroalgae compared to those with canopy-forming macroalgae. Moreover, the presence of two macroalgal taxa, Sargassum and Lobophora, within the plots was associated with lower coral recruitment to the tiles. Coral size frequency distribution in Florence Bay showed similar trends, with smaller corals (< 20 cm diameter) only present in areas with low density and height of canopy-forming macroalgae and, in particular, low abundance of Sargassum. We thus suggest that both the structure and composition of the macroalgal community drive, at some point, coral replenishment dynamics
Large language models vs human for classifying clinical documents
No full textBackground: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and Systematized Nomenclature of Medicine (SNOMED) mapping has shown promise in performing these classifications. However, challenges remain, particularly in reducing false negatives, where certain diagnoses are not correctly identified by either approach.
Objective: This study explores the potential of leveraging advanced large language models to improve the accuracy of ICD-10 classifications in challenging cases of medical records where machine learning and SNOMED mapping fail.
Methods: We evaluated the performance of ChatGPT 3.5 and ChatGPT 4 in classifying ICD-10 codes from discharge summaries within selected records of the Medical Information Mart for Intensive Care (MIMIC) IV dataset. These records comprised 802 discharge summaries identified as false negatives by both machine learning and SNOMED mapping methods, showing their challenging case. Each summary was assessed by ChatGPT 3.5 and 4 using a classification prompt, and the results were compared to human coder evaluations. Five human coders, with a combined experience of over 30 years, independently classified a stratified sample of 100 summaries to validate ChatGPT's performance.
Results: ChatGPT 4 demonstrated significantly improved consistency over ChatGPT 3.5, with matching results between runs ranging from 86% to 89%, compared to 57% to 67% for ChatGPT 3.5. The classification accuracy of ChatGPT 4 was variable across different ICD-10 codes. Overall, human coders performed better than ChatGPT. However, ChatGPT matched the median performance of human coders, achieving an accuracy rate of 22%.
Conclusion: This study underscores the potential of integrating advanced language models with clinical coding processes to improve documentation accuracy. ChatGPT 4 demonstrated improved consistency and comparable performance to median human coders, achieving 22% accuracy in challenging cases. Combining ChatGPT with methods like SNOMED mapping could further enhance clinical coding accuracy, particularly for complex scenarios
Inspirational Entrepreneurship and Stimulating Tourism: Lessons from the Himalayas in India
No full textThis book explores inspirational entrepreneurial activities in the tourism industry of Ladakh, India. It particularly looks at ways to develop an entrepreneurial yet environment-friendly tourist destination. The book starts off with in-depth historical reflections of entrepreneurship and tourism in one of India’s fastest growing tourist destinations. Subsequently, the book studies the unique entrepreneurial challenges and opportunities in Ladakh’s extreme resource-scarcity and remote context based on empirical evidence from entrepreneurs and stakeholders in the tourism industry. In addition, a broad overview of contemporary entrepreneurial activities in the tourism industry in Ladakh is presented, underscoring the importance of indigenous knowledge and cultural traditions for developing sustainable tourism. Based on qualitative data analyses and literature reflections, this book provides scholars, students, professionals and policymakers an alternative view on entrepreneurial activities in the tourism industry of an ecologically jeopardized region
Negative emotional states and technological addictions: The buffering and paradoxical role of perceived social support
No full textObjective: Negative emotional states are well-established risk factors for technological addictions because some individuals use games, social media, or pornography excessively as a coping strategy. Given these links, perceived social support should act as a buffer against the effects of negative emotional states. Consequently, the current exploratory study aimed to examine the role of perceived social support in moderating the effects of negative emotional states on technological addictions.
Methods: There was a total of 169 participants (71.6% females, 27.2% males, and 1.2% others). They completed instruments that assess negative emotional states, perceived social support, internet gaming disorder (IGD), social media addiction (SMA), and problematic pornography use (PPU).
Results: The results showed that perceived social support had buffering effects (reducing symptoms of technological addictions), paradoxical effects (exacerbating symptoms of technological addictions), and no significant effects. Specifically, individuals with low negative emotional states had lower PPU with perceived social support from significant other and family. However, individuals with high negative emotional states had higher IGD and PPU with perceived social support from family.
Conclusion: Limitations include the lack of distinction between online and offline perceived social support and the omission of the last item of the instrument for PPU. Limitations notwithstanding, the study extended on previous research and highlighted the complex relationships between negative emotional states, perceived social support, and technological addictions
The effect of wet season river flows on flood plume distribution across northern Australia; contribution to coastal productivity, and future extent under climate change
No full textNorthern Australia is home to some of the world’s most expansive and ecologically significant river systems, including the Flinders, Gilbert, and Daly Rivers. These river catchments, which drain into the Gulf of Carpentaria and the Timor Sea, are vital to both the health of the region’s estuaries, coastal ecosystems and its commercial fisheries. This study investigates the relationship between wet season river flows, flood plume extent, and primary productivity in adjacent coastal seas, with a focus on understanding potential impacts from climate change and water extraction on these dynamics. Hydrological data from 2003 to 2023 was analysed for the Flinders, Gilbert, and Daly Rivers to determine peak flow events and their corresponding flood plume sizes using MODIS satellite imagery. The study found that flood plumes were highly variable across the 20-year period, with significant events recorded in 2019 and 2023 and strong relationships between 7-day river flows and plume extents for all rivers. Chlorophyll-a concentration, as a proxy for primary productivity, was significantly associated with plume sizes in the southern Gulf of Carpentaria and Anson Bay, specifically for tertiary plumes from the Flinders, Gilbert, and Daly Rivers. Future climate projections indicate potential reductions in rainfall by 2070–2099, which could lead to decreases in flood plume extent and associated primary productivity. This research highlights the critical connection between river flows, coastal flood plumes, and marine productivity in northern Australia. The findings underscore the importance of maintaining environmental water flows to sustain coastal ecosystems and fisheries, particularly in the context of increasing water allocation pressures and the potential impacts of climate change on regional rainfall patterns
High-Resolution Histopathology Whole Slide Image Generation Using Wavelet Diffusion Model
No full textRecent advances in deep learning (DL) have significantly improved computational pathology, particularly for analyzing tissue images in diagnostic and prognostic tasks. However, most DL methods depend on large-scale annotated datasets, which are difficult to obtain in the medical field due to the time, cost, and labor involved in annotation. To address this, generative models, especially diffusion models, have been investigated to create synthetic whole-slide images (WSIs).
However, generating high-quality histopathology WSIs is challenging due to their gigapixel resolution. While diffusion models produce excellent images, they suffer from slow inference speeds, limiting their practical application. In this work, we propose a Wavelet-Diffusion Model (WDM) that integrates wavelet transforms into diffusion models, enhancing sampling efficiency without compromising image quality. The resulting framework, WSI-WDM, was evaluated on two public datasets: PAIP2019 (liver cancer segmentation) and BCSS (breast cancer semantic segmentation).
Our experimental results show that WSI-WDM outperforms state-of-the-art methods in both generation quality and inference speed. Specifically, it achieved Frechet Inception Distance (FID) scores of 268.55 for PAIP2019 and 303.02 for BCSS, with inference times of 300.05 ± 0.84 ms and 275.17 ± 0.72 ms, respectively, demonstrating that WSI-WDM provides an efficient, high-quality solution for generating synthetic WSIs