Explanations are a fundamental element of how people make sense of the political world. Citizens routinely ask and answer questions about why events happen, who is responsible, and what could or should be done differently. Yet despite their importance, explanations remain an underdeveloped object of systematic analysis in political science, and existing approaches are fragmented and often issue-specific. I introduce a framework for detecting and parsing explanations in political text. To do this, I train a lightweight causal language model that returns a structured data set of causal claims in the form of cause-effect pairs for downstream analysis. I demonstrate how causal explanations can be studied at scale, and show the method's modest annotation requirements, generalizability, and accuracy relative to human coding.
Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This survey reviews transformer-based LLM applications across scientific fields such as biology, medicine, chemistry, and meteorology, underscoring their role in advancing research. However, the continuous expansion of model size has led to significant memory demands, hindering further development and application of LLMs for science. This survey systematically reviews and categorizes memory-efficient pre-training techniques for large-scale transformers, including algorithm-level, system-level, and hardware-software co-optimization. Using AlphaFold 2 as an example, we demonstrate how tailored memory optimization methods can reduce storage needs while preserving prediction accuracy. By bridging model efficiency and scientific application needs, we hope to provide insights for scalable and cost-effective LLM training in AI for science.
Community science observational datasets are useful in epidemiology and ecology for modeling species distributions, but the heterogeneous nature of the data presents significant challenges for standardization, data quality assurance and control, and workflow management. In this paper, we present a data workflow for cleaning and harmonizing multiple community science datasets, which we implement in a case study using eBird, iNaturalist, GBIF, and other datasets to model the impact of highly pathogenic avian influenza in populations of birds in the subantarctic. We predict population sizes for several species where the demographics are not known, and we present novel estimates for potential mortality rates from HPAI for those species, based on a novel aggregated dataset of mortality rates in the subantarctic.
The launch of Postdigital Science and Education helped generate a burst of new scholarship about this emerging turn in educational research and theory. Yet, what it means to do postdigital research remains obscure to many. Ongoing debates around definitions, combined with the complexity of analysing digital activity within rich contexts that are also social, material, political, economic, and so on, make it challenging to understand what constitutes postdigital research. Meanings of the postdigital emerge from within the processes of postdigital research. Furthermore, while some individual contributions to postdigital research may be grounded in particular disciplines, we argue that postdigital research, in general, benefits from transdisciplinary knowledge. All of this points to a need for flexibility, and principled, rather than prescriptive, research and scholarship practices. It situates postdigital research in the tradition of compositional and inventive research approaches, and this paper traces that relationship.
The next generation of ground-based gravitational-wave interferometers is expected to generate a bounty of new astrophysical discoveries, with sensitivities and bandwidths greatly improved compared to current-generation detectors. These detectors will allow us to make exceptional advancements in our understanding of fundamental physics, the dynamics of dense matter, and the cosmic history of compact objects. The fundamental design aspects of these planned interferometers will enable these new discoveries; however, challenges in technical noise, data quality, and calibration have the potential to limit the scientific reach of these instruments. In this work, we evaluate the requirements of these elements for next-generation gravitational-wave science, focusing on how these areas may impact the proposed Cosmic Explorer observatory. We highlight multiple aspects of these fields where additional research and development is required to ensure Cosmic Explorer reaches its full potential.
Michael Burnham, Kayla Kahn, Ryan Yank Wang
et al.
Social scientists quickly adopted large language models due to their ability to annotate documents without supervised training, an ability known as zero-shot learning. However, due to their compute demands, cost, and often proprietary nature, these models are often at odds with replication and open science standards. This paper introduces the Political DEBATE (DeBERTa Algorithm for Textual Entailment) language models for zero-shot and few-shot classification of political documents. These models are not only as good, or better than, state-of-the art large language models at zero and few-shot classification, but are orders of magnitude more efficient and completely open source. By training the models on a simple random sample of 10-25 documents, they can outperform supervised classifiers trained on hundreds or thousands of documents and state-of-the-art generative models with complex, engineered prompts. Additionally, we release the PolNLI dataset used to train these models -- a corpus of over 200,000 political documents with highly accurate labels across over 800 classification tasks.
This paper discusses the argumentative turn amongst farmers and the other different stakeholders in the case of land disputes, Kebumen, Central Java, Indonesia. While policy makers insisted that the land function conversion from agriculture and tourism to mining was needed to support local development as through the absorption of labors into employment sector, as well as to improve local people’s income, local farmers insisted that the conversion merely uprooting their ownership of land and let them back to periods where they were jobless and lack of source of income decades ago. This paper applied qualitative research supported with observation and interviews with parties involved in the case, to highlight the argumentative turn within land policy, which in the case of Kebumen leads to policy conflict. This paper identifies the elite-driven policy in the land dispute cases in Kebumen has led policy close to discussions with various stakeholders, which are necessary to be heard in the policy making. This finding highlights the idea that policy creates within itself politics that is in-line with the interest of the elites, and yet, resulted in the feedback loop, manifested through the strong resistance of the community.
Political institutions and public administration (General)
A afirmativa de que a Constituição Federal de 1988 garante o direito e o acesso das crianças pequenas à educação infantil, por meio das ações positivadas do Estado democrático brasileiro, precisa ser reiterada. Trazer à tona os compromissos, por meio dos dispositivos legais, com os cidadãos de pouca idade é urgente, sobretudo em tempos de ataque frontal à jovem democracia brasileira, no sentido de construir a totalidade dos dizeres legais na sua origem constitucional e na efetivação do direito no acesso e na oferta. Nessa perspectiva, o objetivo do artigo é levantar e analisar a oferta de vagas públicas para a educação infantil no estado do Tocantins, Brasil, entre os anos de 1990 e 2020, descortinando a evolução do acesso e a subtração do benefício para muitas crianças tocantinenses.
The aim of this study was to obtain in-depth information about the relationship between power in the general election of legislative candidates 2014 based on the typology actualized in political discourse transactional in Makassar city. The type of research was qualitative by using a constructivism hermeneutics approach. In collecting the data, the researchers using data discourse through the print media, as well as researchers act as a research instrument. Data collected was focussed on the relation of power in the general election of legislative candidates in 2014 and contestation pattern of legislative candidate transactional political discourse. Data was collected through in-depth interviews. The data were then analyzed using the technique of interactive analysis intended to look at the process, meaning, and power relations in the political discourse transactional. The results showed that the pattern of power relations affect the closeness of political actors with a number of officials / local authorities in the city of Makassar. Proximity is seen in the pattern of relationships among political party leaders resulted in their patterns of cooperation in the use of power to give effect to the citizens (voters) pattern is also visible proximity of the proximity of political actors who want to give effect to the citizens as a form of support.
This thesis aims to study the political change taking place in Vietnam. The analysis was based on a thorough systematic review, including the main articles and data on the topic. The analytic perspective adopted was that of polyarchy, which acted as a lens subsequently used to answer two questions. First: What are the points of contact between economic development, political liberalization, and civil society? And second: What political factors lead to the consideration of Vietnam as a polyarchy?
Mahyuddin K. M. Nasution, Rahmat Hidayat, Rahmad Syah
Possible for science itself, conceptually, to have and will understand differently, let alone science also seen as technology, such as computer science. After all, science and technology are viewpoints diverse by either individual, community, or social. Generally, it depends on socioeconomic capabilities. So it is with computer science has become a phenomenon and fashionable, where based on the stream of documents, various issues arise in either its theory or implementation, adapting different communities, or designing curriculum holds in the education system.
Vinod Kumar Chauhan, Anna Ledwoch, Alexandra Brintrup
et al.
Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines' reputations, waste passengers' time and money, and directly impact the environment. This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline. We aim to determine the most disruptive airports, flights, flight-connections, and connection types in an airline network. Disruptive elements are influential or critical entities in an airline network. They are the elements that can either cause (airline schedules) or have caused (historical data) the largest disturbances in the network. An airline can improve its operations by avoiding delays caused by the most disruptive elements. The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline. The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations. The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights. Finally, we observed that passenger connections between flights are the most disruptive connection type. Therefore, the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.