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Transparency or Map-Washing? Digital Geospatial Visualisation Tools in the Palm Oil Industry
Get PDFWe introduce the notion of map-washing and ask whether digital geospatial visualisation (DGV) tools distort information or provide greater supply chain transparency. Map-washing explains a process of disclosing spatial information that has little or no value to the intended users, but rather creates, conforms to or distorts a particular narrative. In the context of advancements in satellite technology, cloud-based geographic information systems and sophisticated web-based digital programming, we observe the rise of sophisticated web-based tools that offer geospatial visualisations of business activities. Firms across a broad range of agro-commodities are investing in DGV tools as part of efforts to achieve greater levels of transparency in their operations. The function of these tools, their intended audiences and the broader environmental and social outcomes remain unclear. Our research is based on a desk-based analysis of DGV tools employed across the palm oil industry, and interviews with informed stakeholders in the palm oil and related industries. From 97 companies assessed in the study, we identified 16 companies with active DGV tools. We found that companies employ a spectrum of geospatial visualisation tools that differ in the technologies used, data inputs, level of interactivity, type of collaborations and the outcomes and degree of stakeholder participation. We argue that the spatialisation of palm oil supply chains achieves a sophistication in corporate communication that is more difficult to achieve with traditional CSR reporting. Yet we also contend that the transformative power of these tools is open to debate, arguing that map-washing may deflect attention away from negative externalities. We propose guidelines and regulation as a means to enhance the positive contributions of DGV tools to sustainability and transparency
Understanding capabilities, opportunities, and motivations of walking for physical activity among adults with intellectual disabilities: A qualitative theory-based study
Get PDFAbstract
Background
This study aimed to apply the COM-B model to understand the capabilities, opportunities and motivations for walking behaviour among adults with intellectual disabilities.
Methods
A qualitative study was conducted with adults (≥18 years) with mild to moderate intellectual disabilities living in Greater Glasgow using one-to-one interviews (n=12; women=5) and a photo-elicitation activity followed by a focus group discussion (n=5; women=1). The framework approach to analysis allowed for influences of walking to be mapped onto the COM-B model.
Results
Walking is a complex behaviour with many capabilities, opportunities and motivations to consider. Adults with intellectual disabilities were involved in making decisions about what results should be prioritised.
Conclusions
The COM-B model is a flexible framework that can be applied to understand health behaviours of adults with intellectual disabilities. It is imperative to work with adults with intellectual disabilities throughout the research process
Reply to letter to the editor: Predicting coronary artery disease using machine learning with optimised feature selection- methodological clarifications for CAD prediction study.
No full textCapturing the process of knowledge creation: creative approaches for disrupting IMRaD in PhD theses and duoethnography articles
Get PDFThis duoethnography explores our use of creative writing in our education doctoral theses by taking an emergent and experimental approach, including written and verbal dialogues and knitted narrative (Heydon, 2010). Duoethnographies should be transparent in their processes (Burleigh and Baum, 2022) and open to different interpretations (Norris and Sawyer, 2012). However, IMRaD (Introduction, Methodology, Results and Discussion) as a guiding structure in the writing of duoethnographies elides underpinning processes and closes down the potential for meaning-making. Our innovative approach to duoethnography enables us to arrive at new understandings of the relationships between our identities, our writing and our ethical practices. We also reflect on how written and verbal dialogues offer different affordances for reflection and transformation in duoethnographies. By deliberately presenting our duoethnography as disrupting IMRaD, we show other ethnographers how they can become more transparent about processes and open up the potential for multiple interpretations
Universities unbound: Universities as sites of human rights activism and protection in an era of democratic crisis
Get PDFThis article champions the potential for universities to play an enhanced role supporting human rights activism and protection in the context of democratic crisis. The challenges such an agenda faces are significant. In addition to global trends such as democratic backsliding and shrinking civic and political space, universities themselves exhibit “two faces,” as sites of violence and exclusion as well as of more progressive values, and are caught between the pincer movement of privatization and increasing state interference. However, universities often enjoy more autonomy than civil society groups. Drawing on core values such as academic freedom and social justice, and particular qualities—legitimacy, status, access to knowledge, resources, and local and global networks—universities have both the potential and the responsibility to act. The article identifies four roles universities can play in relation to activism and protection: instigators, incubators (of ideas, values, and organizations), collaborators, and protectors. Three forms of protection—of people, values, and knowledge—are interdependent, with activists more likely to feel protected if their values and knowledge are reflected within universities. Ultimately, if universities do not support others, who will be left to defend them when attacks intensify on universities themselves
Choreomusicology: Dialogues in Music and Dance
No full textChoreomusicology: Dialogues in Music and Dance is a distinguished collection of chapters by leading scholars presenting research that redefines and rethinks the question of what dance and music are, together and apart, and which promotes new ideas and voices in the discipline.
Focusing on matters historical, critical, and conceptual, and defining dance-music interactions from the era of aristocratic court dance to the present, the book covers a wide range of topics, including dance and music performance practice, queer studies, colonialism and exoticism, disability studies, the “reparative” humanities, and film. The volume is organized into two sections: Part 1 examines theoretical and conceptual issues, including theories of embodiment, musicality, and dance aesthetics, with examples including contemporary ballet, the role of the conductor, and even fountains in Las Vegas. In Part 2, contributors consider choreomusicology as a historical discipline and tackle the problem of musical and choreographic reconstruction, from medieval dance to reimagining lost music in early experiment in dance film, as well as choreomusical analyses of twentieth-century works.
Capturing the breadth of studies and approaches that are encompassed in choreomusicology, this book will be of interest to students and scholars in the fields of dance and media studies, musicology, and ethnomusicology, as well as appealing to dancers, choreographers, musicians, and composers looking for new approaches to thinking about music and dance
Secure Medical Data Transmission Model for Distance Medical Care Systems
No full textIn the era of digital healthcare, the demand for remote medical care systems has surged, necessitating the secure and efficient transmission of sensitive medical data over networks. This research paper presents a comprehensive model for the secure transmission of medical data in distance medical care systems. The proposed model addresses the critical challenges of data privacy, integrity, and availability while ensuring real-time accessibility for healthcare providers and patients. Our model leverages advanced encryption techniques and authentication mechanisms to protect the confidentiality of medical data during transmission. Additionally, it employs error-checking and redundancy measures to ensure the integrity of the data being transmitted, preventing unauthorized alterations. To validate the effectiveness of our proposed model, we conducted extensive simulations and real-world experiments. The results demonstrate that our approach not only meets the stringent security and privacy requirements of healthcare data but also offers a seamless and efficient data transmission experience for remote medical care systems
Birth control in Africa: study tracks the use of long-acting contraception in 26 countries
No full textEnhancing E-commerce Security: A Hybrid Machine Learning Approach to Fraud Detection
Get PDFIn the rapidly expanding e-commerce landscape, ensuring the security of transactions is essential to maintain consumer trust. However, the challenge of accurately distinguishing between genuine and fraudulent transactions persists, largely due to issues such as dataset imbalance, suboptimal feature selection, and varying algorithm performance. As such, this study aims to enhance fraud detection accuracy by developing a hybrid model that combines an artificial neural network (ANN) with a deep neural network (DNN), employing the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and linear discriminant analysis (LDA) for effective feature extraction. By integrating SMOTE with LDA, the model is trained to better handle imbalanced datasets and extract relevant features, thereby improving its predictive capabilities. Our results demonstrate that the hybrid model outperforms individual models, achieving a precision rate of 95.46% and an area under the curve (AUC) score of 97.04%. In comparison, the stand-alone ANN model recorded an accuracy of 95.46% and an AUC of 96.92%, while the DNN achieved a success rate of 95.01% and an AUC of 97.17%. These outcomes highlight the significant advantages of combining advanced feature extraction and class imbalance techniques, resulting in superior detection performance. The study concludes that the hybrid model provides a robust solution for improving fraud detection in e-commerce, offering a reliable approach to differentiate between genuine and fraudulent transactions effectively. This approach not only addresses existing challenges but also sets a foundation for future research in enhancing transaction security through innovative deep learning methodologies