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The Flying Ace as a Hero: An Analysis of the Representations of Douglas Bader and Erich Hartmann
Get PDFThis study develops a four-factor framework (4F model) based on fear, suffering, values, and characteristics for analyzing depictions of physical-risk martial heroism. Specifically, this framework is applied to assess representations of two World War II flying aces, the Royal Air Force pilot Douglas Bader and his German contemporary from the Luftwaffe, Erich Hartmann. On investigation, it is revealed that Bader and Hartmann are either described as fearless or capable of continuing their heroic journey despite their fears. Moreover, the literature contends that both these pilots experienced immense suffering but eventually overcame them. Regarding values, Bader is portrayed as a conservative patriot, whereas Hartmann is said to be a romantic and chivalrous hero. In terms of their characteristics, the depictions of Bader highlight both his positive and negative traits, whereas the darker sides of the hero are virtually absent in the rosy representations of Hartmann
Wiring the community health worker: A winning strategy for NCD care
No full textExecutive summary
Expanding access to care for those with non- communicable diseases is accelerated when technology works with – and for – people.
In an increasingly connected and digital world, technology continues to transform even the most human of interactions, including healthcare. Yet ascertaining how innovation will affect people – and whether that change will be positive for all or just
a few – has historically been challenging. Most recently, the rise of artificial intelligence (AI) has generated significant attention and enthusiasm, in part due to its ability to expedite transactional processes and automate mundane tasks. Whether digital transformation and AI will ultimately enhance or detract from health outcomes and social interactions remains to be seen, and the future role of human beings in delivering healthcare is likewise still uncertain. A recent publication from the World Economic Forum outlines several high-potential use cases of AI in healthcare, the barriers to realizing value and the principles for accelerating adoption. What is not in doubt is that the digital revolution in healthcare is well under way.
While new technologies are poised to disrupt people’s experience of healthcare, the speed and allocation of the resources for innovation remain unbalanced. The digital health market has received unprecedented levels of funding over the past decade, and many are now asking difficult questions about the use of these funds and whether investment has been prudent, effective or equitably distributed. Global investment in digital health
is disparate, and is skewed heavily towards the United States. Yet within the US there are additional disparities; for instance, in the funding of start-ups with female and/or ethnic minority founders.2,3 To truly improve the state of health around the world, addressing this reality and these imbalances is essential, as is reflection on both the potential and limits of technology to change health outcomes. In judging whether technological innovation will stimulate positive change in healthcare, it matters not only what and how change is implemented but also who is being supported to design and create the transformation
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues
No full textWith the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence. However, canonical fact verification tasks do not apply to catching subtle differences in factually consistent claims, which might still bias the readers, especially on contentious political or economic issues. Our underlying assumption is that among the trusted sources, one’s argument is not necessarily more true than the other, requiring comparison rather than verification. In this study, we propose ClaimDIff, a novel dataset that primarily focuses on comparing the nuance between claim pairs. In ClaimDiff, we provide human-labeled 2,941 claim pairs from 268 news articles. We observe that while humans are capable of detecting the nuances between claims, strong baselines struggle to detect them, showing over a 19% absolute gap with the humans. We hope this initial study could help readers to gain an unbiased grasp of contentious issues through machine-aided comparison
KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications
No full textLarge language models (LLMs) not only learn natural text generation abilities but also social biases against different demographic groups from real-world data. This poses a critical risk when deploying LLM-based applications. Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups. This limitation requires localized social bias datasets to ensure the safe and effective deployment of LLMs. To this end, we present KosBi, a new social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories. We find that through filtering-based moderation, social biases in generated content can be reduced by 16.47%p on average for HyperClova (30B and 82B), and GPT-3
First CLAS12 Measurement of Deeply Virtual Compton Scattering Beam-Spin Asymmetries in the Extended Valence Region
No full textDeeply virtual Compton scattering (DVCS) allows one to probe generalized parton distributions describing the 3D structure of the nucleon. We report the first measurement of the DVCS beam-spin asymmetry using the CLAS12 spectrometer with a 10.2 and 10.6 GeV electron beam scattering from unpolarized protons. The results greatly extend the 2 and Bjorken- phase space beyond the existing data in the valence region and provide 1600 new data points measured with unprecedented statistical uncertainty, setting new, tight constraints for future phenomenological studies
Optimal method for reconstructing polychromatic maps from broadband observations with an asymmetric antenna pattern
No full textBroadband time-ordered data obtained from telescopes with a wavelength-dependent, asymmetric beam pattern can be used to extract maps at multiple wavelengths from a single scan. This technique is especially useful when collecting data on cosmic phenomena such as the cosmic microwave background (CMB) radiation, as it provides the ability to separate the CMB signal from foreground contaminants. We develop a method to determine the optimal linear combinations of wavelengths (“colors”) that can be reconstructed for a given telescope design and the number of colors that are measurable with high signal-to-noise ratio. The optimal colors are found as eigenvectors of a matrix derived from the inverse noise covariance matrix. When the telescope is able to scan the sky isotropically, it is useful to transform to a spherical harmonic basis, in which this matrix has a particularly simple form. We propose using the optimal colors determined from the isotropic case even when the actual scanning pattern is not isotropic (e.g., covers only part of the sky). We perform simulations showing that maps in multiple colors can be reconstructed accurately from both full-sky and partial-sky scans. Although the original motivation for this research comes from mapping the CMB, this method of polychromatic mapmaking will have broader applications throughout astrophysics
Elusive decolonisation of IR in the Arab world
No full textArab social science scholarship, and IR in particular, has been systematically underfunded and sidelined by governments across the region. As such, IR scholars in the Arab world have struggled to produce scholarship in hostile and authoritarian environments, let alone address efforts to decolonise. Of the few initiatives of indigenising social science that exist in the Arab world, the Doha Institute for Graduate Studies (DI) and its founding institution, the Arab Center for Research and Policy Studies (ACRPS), are the main examples. In this intervention, I will review the attempts to indigenise and decolonise IR within these institutions. I focus on how the DI is implementing three main approaches: increasing access to the discipline, rethinking how we teach IR, and facilitating theory production from the region. I demonstrate the strengths and weaknesses of the three abovementioned approaches by drawing attention to performative measures on the part of regional scholars, and pretending localism on the part of scholars in the Global North, which together help to perpetuate neomarginalisation. The shortcomings discussed permeate and distort attempts to decolonise the discipline within the Arab world
Consistency among common measures of corporate social and sustainability performance
No full textThe objective of this study is to thoroughly evaluate and compare three popular databases used in research on the environment, society and governance. Our analysis aims to establish construct validity, and thus addresses the difficulty in choosing which data providers to use for this type of research, and whether findings are comparable across the three databases and applicable to firms in different countries. The most widely used database, referred to as KLD (Kinder, Lydenberg, and Domini), is used to help establish the construct validity of measures coming from the other databases, known as Sustainalytics and Asset4. Frequently used measures from the three databases are highly correlated, and tests of the relationships between composite measures from the databases and financial performance are consistently positive. Even a more focused stakeholder management measure doesn\u27t materially alter the results, suggesting that stakeholder-oriented firms also tend to be socially responsible in ways beyond stakeholder relationships, such as in preserving the environment. We also find that Sustainalytics and Asset4 track substantially more variables and contain many more international firms than KLD, making them more flexible for empirical work on this topic
The Ghosts That Haunt Us: An unsubstantiated hypothesis
No full textSubmitted for the October 2023 prompt: Machine in the Ghos
