Jurnal Online STTKD (Sekolah Tinggi Teknologi Kedirgantaraan)
Not a member yet
77141 research outputs found
Sort by
Comparing the Accuracy of Three Predictive Information Criteria for Bayesian Linear Multilevel Model Selection
No full textBayesian multilevel modeling techniques have become increasingly popular. As researchers leverage these techniques, information criteria—fit indices which provide information about a model’s fit to the data—play an important role in disambiguating between competing models. The deviance information criteria (DIC) has been historically popular and is computationally easy, yet newer indices such as Watanabe-Akaike information criterion (WAIC) and an approximation to the leave-one-out cross-validation information criterion (LOO-CV) have been recently introduced. However, researchers may be unsure about which criteria to use, as to our knowledge, a systematic evaluation of these Bayesian criteria in a multilevel context has not yet been undertaken. Complicating this matter, computation of these indices using the so-called marginal likelihood is sometimes recommended, yet use of the conditional likelihood is easier and more readily found in some popular software. In addition, researchers frequently select the model with the lowest value of the information criteria, discounting the presence of uncertainty in calculating the criteria. Across two extensive simulation studies meant to mimic experimental and observational studies, we investigate the model selection accuracy of conditional and marginal versions of DIC, WAIC, and LOO-CV; we also compare a lowest wins strategy versus one that considers model selection uncertainty. In general, indices based on the marginal likelihood had a slight advantage and performed similarly to each other, whereas under the conditional likelihood WAIC and LOO-CV outperformed DIC. In addition, we argue that a selection strategy that simply chooses the model with the lowest information criteria may result in overfitting
Reverse-engineering the centered self
No full textIn certain problem solving contexts, people organize their domain through treating themselves as the perceptual and cognitive center of their world. They identify and solve a particular problem from their perspective as a particular agent, with a particular location, at a particular time, in a particular environment. When they do this, selecting and solving problems from their perspective as an agent, they engage in a distinctive kind of agent-centered problem solving. Partially Observable Markov Decision Processes (POMDPs), a framework for modeling decision-making in uncertain environments unfolding over time, have effectively become a "standard model" of intelligent agency. Yet, as these models are ordinarily interpreted, they do not explicitly represent agent-centered problem solving. Accordingly, to model this type of problem solving, we begin by extending the standard POMDP framework to define “ePOMDPs.” This formalism models how an agent, once it centers itself on a particular self-and-world representation, plans and acts rationally from its own perspective. To capture the way that such agents choose which problem to solve, we build on our ePOMDPs to develop a “meta-ePOMDP” agent within a hierarchical Bayesian framework. We implement our meta-ePOMDP agents for two different suites of “centering game” tasks which highlight different aspects of our theory. We find that our models explain signatures of agent-centered problem solving not captured by alternative models, in particular, the difficulty of navigating spaces of possible problem representations. We close by suggesting that our model could provide the beginnings of a computational framework for a person to have a self
Establishing trust in automated reasoning
No full textSince its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by early practitioners of computational science, but was later forgotten. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures
Another Earth: An astronomical concept of the planet for the environmental humanities
No full textSince the notion of the Anthropocene entered the discourse of environmental humanities, it has prompted multiple conceptual innovations. This paper focuses on one such case: the term planetary – and the adjacent genre of planetary thinking – theorized by a broad range of scholars. The original contribution of this paper lies in developing an astronomical concept of the planet, derived from the definition agreed by resolution 5A 2006 of International Astronomical Union, which defines planets based on their dynamical context. By means of philosophical interpretation of the definition’s underlying assumptions, this paper articulates standalone philosophical implications of the astronomical concept: the contextualization of the planet in expanded ecology of the solar system, paired with the understanding of the planet as a structure of phase gradients and as a historical natural kind. Furthermore, I position the astronomical concept alongside theorizations of the planet by Latour, Stengers, Clark & Szerszynski as well as Chakrabarty, touching upon the Gaia hypothesis or the concept of geological history. Finally, I encourage deeper disciplinary interaction between astronomy and environmental humanities. The benefits of this interaction are highlighted in the concluding discussion concerning the multiplicity of planetary narratives and applications of the astronomical concept of the planet
How can selective processing of vaccination information be diminished? Effects of mindsets and kinds of information
No full textBackground: Selective processing of attitude-consistent information is a substantial obstacle in convincing vaccine-skeptical people of the benefits of vaccinations. This study tests (i) which types of information are particularly prone to such selective information processing, and (ii) whether a deliberative (vs. implemental) mindset focusing on potential benefits and harms may diminish its effects.
Design: 612 Mturk participants were randomized into an implemental or deliberative mindset and received a flu vaccine-skeptical narrative, a flu vaccination facts box transparently summarizing risks and benefits, and a message by the Center for Disease Prevention and Control (CDC) in favor of the flu vaccine either referring to COVID-19 or not. We tested how these variations affected the acceptance of and the willingness to share each message. Furthermore, we evaluated their impact on flu vaccination attitudes and intentions.
Results: The mindset manipulation failed to diminish generally prevalent selective information processing. While vaccine-skeptics did not accept and like the CDC message referring to COVID-19 (particularly in a deliberative mindset), they generally accepted the vaccination facts box more readily compared to both CDC messages.
Conclusion: While mindsets were ineffective, more general and transparent information may be more likely to reach an anti-vaccine audience
Co-producing a new scale with young people aged 10 – 24 years: A protocol for the development and validation of the Youth Loneliness Scale (YLS)
No full textBackground: Around half of young people aged 10-24 years in the United Kingdom report feeling lonely “often” or “some of the time”, with similar rates in other countries. These experiences of loneliness are linked to well-being and a wide range of adverse physical and mental health outcomes. However, our understanding of the aetiology and sequelae of youth loneliness, as well as the development of preventative measures and interventions, has been hampered by a lack of scales that can accurately capture the authentic experiences of young people.
Methods: Here, we provide a protocol for developing and validating an age-sensitive loneliness scale for young people aged 10-24 years: the Youth Loneliness Scale (YLS). The scale will be designed to measure loneliness in the general population of young people in the United Kingdom. The scale development process will follow a multi-step approach, going from item generation to psychometric evaluation. Item generation will include a combination of verbal and non-verbal techniques to enable broad expression of what it means to be lonely as a young person. The scale has been and will be co-produced with young people from design to dissemination.
Discussion: The protocol provided here allows researchers to evaluate the final scale generated against the plans set out here. We also encourage the use and adaptation of the protocol to develop age-sensitive loneliness scales in other cultural contexts and for other populations
Positioning the “F-Words for Child Development” in the scope of speech-language pathology to support childhood participation
No full textThis paper explores how the “F-Words for Child Development” framework, a practical translation of the International Classification of Functioning, Disability and Health (ICF) introduced by Rosenbaum & Gorter (2012), can be applied by speech-language pathologists to enhance communication and mealtime participation for children with disabilities. Each F-Word is explored as a lens for aligning SLP services with real-world participation in mind. We highlight how SLPs can apply the F-Words through interdisciplinary collaboration and family-centered care while recognizing barriers to implementation
How Geopolitical Crisis Events Affect Public Opinion on Foreign Policy: Evidence from the 2022 Russian Invasion of Ukraine
No full textHow do dramatic international crisis events change ordinary citizens’ foreign policy preferences, and how long might these changes last? Scholarship on public opinion has found that the public’s views on foreign policy are generally remarkably consistent and coherent. However, due to the relative rarity of extreme foreign policy events, evidence on how they might affect and change these views and preferences is scare. We present experimental evidence from the February 24, 2022 Russian invasion of Ukraine, a watershed moment for the post-World War II international order that will likely shape global economic and political relations for decades. Our study presents unique panel data from a pre-registered three-wave survey experiment on economic sanctions, collected immediately before, at the height of, and one year into the 2022 Russian invasion of Ukraine. We find that the invasion caused a substantial short-term increase in public support for economic sanctions, much of which persisted one year later. This shows how, in contrast to the usual persistence of public opinion on foreign policy, international crisis events can meaningfully affect preferences in the short and medium term. Our results also provide rare and important insights on the external validity of survey experiments in International Relations. We show that measures taken based on hypothetical scenarios are indeed good indicators of public opinion in moments of real-world foreign policy crisis as it relates to policy priorities
A Bayesian hierarchical joint modeling approach of person and item features that contribute to response bias
No full textTraditionally, extreme responding has been recognized as a bias in which individuals tend to select the highest or lowest response categories on self-report items, regardless of item content. This bias is believed to occur independently of the desirability of the trait or state described in the item statement. However, little effort has been made to empirically test this assumption. In this study, we propose a novel approach to control for both extreme (ERS) and socially desirable responding (SDR) in self-report questionnaires. We considered judges' ratings of item social desirability to control for SDR by employing Bayesian hierarchical and joint modeling. We also considered the effect of ERS to be multiplicative rather than additive, assuming a noncompensatory role to ERS. The advantage of this approach is mostly computational. Overall, our results indicate that modeling the impact of ER improved the model fit on measures of affect and pathological traits. However, contrary to expectations, our analysis did not indicate any substantive influence of social desirability on items’ difficulty. In conclusion, our findings favor the perspective of both ER and social desirability as true traits rather than merely nuisance factors, evidence that our approach may assist researchers in gaining a deeper understanding of response biases in self-report items