Abstract Norway was the first of ten countries to legislate gender quotas for boards of publicly traded firms. There is considerable debate and mixed evidence concerning the implications of female board representation. In this paper, we explain the main sources of biases in the existing literature on the effects of women directors on firm performance and review methods to account for these biases. We address the endogeneity problem by using a difference-in-differences approach to study the effects of women directors on firm performance with specific consideration of the common trend assumption, and we explicitly distinguish between accounting-based (i.e., operating income divided by assets, return on assets) and market-based (i.e., market-to-book ratio and Tobin's Q) performance measures in the Norwegian setting. The control group are firms from Finland, Sweden, and Denmark. We further extend the analysis of causal effects of women directors to firm risk. Our results imply a negative effect of mandated female representation on firm performance and on firm risk.
Many countries are seeking to accelerate their transitions to a zero‑carbon energy system in line with their commitments under the Paris Agreement. In energy policy analysis, transition progress and policy success are often measured by trends in emissions and renewable energy deployment. While these outcome metrics are important, they provide limited insight into the broader systemic changes, as they overlook the underlying drivers and processes. Moreover, existing evaluation frameworks often lack theoretical grounding, leading to an incoherent set of indicators. Here, we assess transition progress from a system-change perspective by developing a theory-driven evaluation framework and applying it to the electricity sectors of four European transition “leaders”: the UK, Germany, Denmark, and Norway. Unlike existing frameworks, our approach is rooted in sustainability transitions literature, improving interpretability while maintaining a focused set of systemic change indicators. Our analysis reveals significant progress in scaling up renewables and phasing out carbon-intensive technologies. However, persistent challenges—particularly in electricity grid infrastructure and regulatory adaptation—continue to hinder full decarbonization, especially in the UK and Germany, which are not on track towards zero‑carbon power. The Norwegian and especially Danish electricity transitions are progressing well, not only in terms of emissions and technology deployment, but the underlying systemic measures make their transition policies credible. Our findings highlight the importance of including systemic metrics, going beyond emissions and renewables deployment metrics, and illustrate the feasibility of a “policy turn” in transition studies through forward-looking analytical tools.
Abstract Introduction Spontaneous abortion is a common pregnancy outcome, but incomplete recording and missing gestational age in health databases pose challenges for research. Accurate timing of the start of pregnancy is critical information in drug safety studies. Objectives To review the literature on database algorithms to estimate gestational length for spontaneous abortions and clinical studies than can inform such algorithms. To estimate the average gestational age for algorithm-identified spontaneous abortions in Norway using interrupted time series analysis. Methods We used an algorithm to identify pregnancies registered in Norway from 2010-2020 and restricted to spontaneous abortions identified from registers of primary and specialist care, and births from the Medical Birth Registry of Norway. For births, we calculated the LMP by subtracting the recorded gestational age from the birth date. We assigned spontaneous abortions gestational ages ranging from 7 to 11 weeks and a corresponding LMP. We identified prescriptions from 70 days before to 97 days after LMP and calculated the number of antidepressant prescriptions per 10,000 pregnancies per day. We applied two-sample interrupted time series analysis with intervention points set at 28 and 55 days after LMP and compared antidepressant prescription trends after 28 gestational days for spontaneous abortions versus births. Results Database algorithms have used estimates for the gestational age at spontaneous abortion ranging from 8-10 weeks, and clinical studies suggest the mean or median gestational age at spontaneous abortion of around 9-10 weeks. In our interrupted time series analysis including 122,495 spontaneous abortions and 631,929 births, the 7-week assumption showed no post-intervention trend, suggesting underestimation. The 9-week assumption closely matched the trend for births (−0.051 prescriptions/day, 95% CI -0.090 to -0.013 vs. -0.056, 95% CI: -0.067 to - 0.046). The 8, 10, and 11-week assumptions showed less precise alignment. The best alignment occurred with the 64-day assumption (9.1 weeks). Conclusion Our study provides an empirically derived estimate for the average gestational age for algorithm-identified spontaneous abortions which can be applied in future research using the same pregnancy algorithm in Norway. While the 64-day estimate seems most accurate for our dataset, further validation studies are necessary to confirm its applicability in other contexts.
In the 1930s, the well-known Norwegian-Italian filmmaker Ivo Caprino attended the Stabekk Gymnasium, at which Olav Storstein was among his teachers. Before his student became internationally famous, Olav Storstein described in his book Fremtiden sitter på skolebenken an episode in which they were both protagonists. The socialist teacher and the young student of Italian origin provide an opportunity to show two opposing educational models: democratic education and fascist authoritarianism. Through a process of inductive analysis that starts from the cited text and crosses archives and reference literature, this essay will highlight the contrast between these two models using for illustrative purposes the biographical episode that allows us to make a historical educational analysis in a comparative perspective, but also to place the episode in a broader context and relate it to relevant aspects of the period. In this story, the interests of fascist propaganda abroad and in Italy, the fascist and socialist educational models, the activities of the Dante Alighieri society, and the private and professional lives of the two protagonists are intertwined.
The literature review is an indispensable step in the research process. It provides the benefit of comprehending the research problem and understanding the current research situation while conducting a comparative analysis of prior works. However, literature summary is challenging and time consuming. The previous LLM-based studies on literature review mainly focused on the complete process, including literature retrieval, screening, and summarization. However, for the summarization step, simple CoT method often lacks the ability to provide extensive comparative summary. In this work, we firstly focus on the independent literature summarization step and introduce ChatCite, an LLM agent with human workflow guidance for comparative literature summary. This agent, by mimicking the human workflow, first extracts key elements from relevant literature and then generates summaries using a Reflective Incremental Mechanism. In order to better evaluate the quality of the generated summaries, we devised a LLM-based automatic evaluation metric, G-Score, in refer to the human evaluation criteria. The ChatCite agent outperformed other models in various dimensions in the experiments. The literature summaries generated by ChatCite can also be directly used for drafting literature reviews.
We formally define the literature (reference) snowballing method and present a refined version of it. We show that the improved algorithm can substantially reduce curator work, even before application of text classification, by reducing the number of candidates to classify. We also present a desktop application named LitBall that implements this and other literature collection methods, through access to the Semantic Scholar academic graph (S2AG).
Identifying the main contributions related to the Automated Test Production (ATP) of Computer Programs and providing an overview about models, methodologies and tools used for this purpose is the aim of this Systematic Literature Review (SLR). The results will enable a comprehensive analysis and insight to evaluate their applicability. A previously produced Systematic Literature Mapping (SLM) contributed to the formulation of the ``Research Questions'' and parameters for the definition of the qualitative analysis protocol of this review.
Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research.
Scientific literature understanding is crucial for extracting targeted information and garnering insights, thereby significantly advancing scientific discovery. Despite the remarkable success of Large Language Models (LLMs), they face challenges in scientific literature understanding, primarily due to (1) a lack of scientific knowledge and (2) unfamiliarity with specialized scientific tasks. To develop an LLM specialized in scientific literature understanding, we propose a hybrid strategy that integrates continual pre-training (CPT) and supervised fine-tuning (SFT), to simultaneously infuse scientific domain knowledge and enhance instruction-following capabilities for domain-specific tasks.cIn this process, we identify two key challenges: (1) constructing high-quality CPT corpora, and (2) generating diverse SFT instructions. We address these challenges through a meticulous pipeline, including PDF text extraction, parsing content error correction, quality filtering, and synthetic instruction creation. Applying this strategy, we present a suite of LLMs: SciLitLLM, specialized in scientific literature understanding. These models demonstrate promising performance on scientific literature understanding benchmarks. Our contributions are threefold: (1) We present an effective framework that integrates CPT and SFT to adapt LLMs to scientific literature understanding, which can also be easily adapted to other domains. (2) We propose an LLM-based synthesis method to generate diverse and high-quality scientific instructions, resulting in a new instruction set -- SciLitIns -- for supervised fine-tuning in less-represented scientific domains. (3) SciLitLLM achieves promising performance improvements on scientific literature understanding benchmarks.
Alexandra Denisa Danciu, Ștefan I. Guțiu, Cătălin Moga
et al.
The network arch bridge (NAB) is a new structural form of arch bridge that was devised 60 years ago by the Norwegian engineer Per Tveit, who is now prof. dr. docent emeritus at the University of Agder, Norway. The network arch is a tied-arch (also known as a bowstring-arch) bridge that combines the benefits of tied-arch bridges and trusses in a single system. While in a classical tied arch, the hangers are vertical, in a network arch, the suspension of the deck to the arch is ensured through a network of inclined hangers that intersect each other at least twice. Thus, the core of the NAB is the hanger arrangement that minimizes the bending moment in the arch to very small values, leading to compression in the arch. Compression with only small bending leads to very slender cross-sections for the elements of the bridge, and deep reductions in terms of materials used and economic and environmental costs. This paper reviews the research into the structural form proposed by Per Tveit and extended by researchers and engineers worldwide. The research methodology included bibliometric literature research, obtained by interrogating the ISI Web of Science (WoS) database and the cited references from the articles on WoS. While the first structural form of a network arch is still in use today and it has proven to be a good idea for spans around 100–120 m, engineers worldwide devised new bridge cross-sections. A brief view of the types of bridge cross-section in use today is given, with details about the bridges chosen as representative. Using analysis of Prof. Tveit’s map, Structurae database and literature review, a database of the network arches around the world was created, emphasizing the development of network arches from the perspectives of continental distribution, opening year, number of structures in different structural forms, and bridge purposes. The structural form was assessed from the perspective of materials used for the arch and the tie, span, purpose and number of lanes, the presence/absence of upper wind-bracings and arch disposition in the vertical plane. In the last part of this review, the newest research into the development of the network arch is discussed. In the past 15 years we have seen an acceleration in network arch development from multiple perspectives: new materials used, such as glulam for the arch or carbon fiber-reinforced plastic for the hangers; span lengths of 250 m and 380 m for large bridge widths; architectural constraints that lead to the outward inclination of the arch, that is pleasing to the eye, but difficult to address from an engineering perspective; the most slender arch bridge in the world, with very slender cross-sections for the arch and the tie.
Biomedical research yields a wealth of information, much of which is only accessible through the literature. Consequently, literature search is an essential tool for building on prior knowledge in clinical and biomedical research. Although recent improvements in artificial intelligence have expanded functionality beyond keyword-based search, these advances may be unfamiliar to clinicians and researchers. In response, we present a survey of literature search tools tailored to both general and specific information needs in biomedicine, with the objective of helping readers efficiently fulfill their information needs. We first examine the widely used PubMed search engine, discussing recent improvements and continued challenges. We then describe literature search tools catering to five specific information needs: 1. Identifying high-quality clinical research for evidence-based medicine. 2. Retrieving gene-related information for precision medicine and genomics. 3. Searching by meaning, including natural language questions. 4. Locating related articles with literature recommendation. 5. Mining literature to discover associations between concepts such as diseases and genetic variants. Additionally, we cover practical considerations and best practices for choosing and using these tools. Finally, we provide a perspective on the future of literature search engines, considering recent breakthroughs in large language models such as ChatGPT. In summary, our survey provides a comprehensive view of biomedical literature search functionalities with 36 publicly available tools.
Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy. We observe that a high-quality catalogue-guided generation process can effectively alleviate this problem. Therefore, we present an atomic and challenging task named Hierarchical Catalogue Generation for Literature Review as the first step for review generation, which aims to produce a hierarchical catalogue of a review paper given various references. We construct a novel English Hierarchical Catalogues of Literature Reviews Dataset with 7.6k literature review catalogues and 389k reference papers. To accurately assess the model performance, we design two evaluation metrics for informativeness and similarity to ground truth from semantics and structure.Our extensive analyses verify the high quality of our dataset and the effectiveness of our evaluation metrics. We further benchmark diverse experiments on state-of-the-art summarization models like BART and large language models like ChatGPT to evaluate their capabilities. We further discuss potential directions for this task to motivate future research.
Tetsu Kasanishi, Masaru Isonuma, Junichiro Mori
et al.
Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literature reviews and 690,000 papers cited in the reviews. Based on the dataset, we evaluate recent transformer-based summarization models on the literature review generation task, including Fusion-in-Decoder extended for literature review generation. Human evaluation results show that some machine-generated summaries are comparable to human-written reviews, while revealing the challenges of automatic literature review generation such as hallucinations and a lack of detailed information. Our dataset and code are available at https://github.com/tetsu9923/SciReviewGen.
Abel Goedegebuure, Indika Kumara, Stefan Driessen
et al.
Data mesh is an emerging domain-driven decentralized data architecture that aims to minimize or avoid operational bottlenecks associated with centralized, monolithic data architectures in enterprises. The topic has picked the practitioners' interest, and there is considerable gray literature on it. At the same time, we observe a lack of academic attempts at defining and building upon the concept. Hence, in this article, we aim to start from the foundations and characterize the data mesh architecture regarding its design principles, architectural components, capabilities, and organizational roles. We systematically collected, analyzed, and synthesized 114 industrial gray literature articles. The review provides insights into practitioners' perspectives on the four key principles of data mesh: data as a product, domain ownership of data, self-serve data platform, and federated computational governance. Moreover, due to the comparability of data mesh and SOA (service-oriented architecture), we mapped the findings from the gray literature into the reference architectures from the SOA academic literature to create the reference architectures for describing three key dimensions of data mesh: organization of capabilities and roles, development, and runtime. Finally, we discuss open research issues in data mesh, partially based on the findings from the gray literature.
Introduction
Little is known regarding how the risk of suicide in refugees relates to their host country. Specifically, to what extent, inter-country differences in structural factors between the host countries may explain the association between refugee status and subsequent suicide is lacking in previous literature.
Objectives
We aimed to investigate the risk of suicide among refugees in Sweden and Norway according to their sex, age, region/country of birth and duration of residence.
Methods
Each suicide case between the age of 18-64 years during 1998 and 2018 (17,572 and 9,443 cases in Sweden and Norway, respectively) was matched with up to 20 population-based controls, by sex and age. Multivariate-adjusted conditional logistic regression models yielding adjusted odds ratios (aORs) with 95% confidence intervals (95% CI) were used to test the association between refugee status and suicide.
Results
The aORs for suicide in refugees in Sweden and Norway were 0.5 (95% CI: 0.5-0.6) and 0.3 (95% CI: 0.3-0.4), compared with the Swedish-born and Norwegian-born individuals, respectively. Stratification by region/country of birth showed similar statistically significant lower odds for most refugee groups in both host countries except for refugees from Eritrea (aOR 1.0, 95% CI: 0.7-1.6) in Sweden. The risk of suicide did not vary much across refugee groups by their duration of residence, sex and age.
Conclusions
The findings of almost similar suicide mortality advantages among refugees in two host countries may suggest that resiliency and culture/religion-bound attitudes could be more influential for suicide risk among refugees than other post-migration environmental and structural factors in the host country.
Disclosure
No significant relationships.
In this paper, we discuss aggregate measures of marginal costs of public funds (MCF) in populations that are heterogeneous with respect to observed as well as unobserved characteristics. We first discuss how to compute MCF in selected examples of traditional (textbook) labour supply models. Next, we review two types of discrete labour supply models proposed in the literature. Subsequently, we discuss how to calculate aggregate measures of MCF for discrete labour supply models. Finally, we apply an estimated two-sector discrete labour supply model to compute MCF based on Norwegian data.
Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval. To tackle the challenges, we demonstrate two tasks along withsolutions, COVID-19 literature retrieval, and question answering. COVID-19 literature retrieval task screens matching COVID-19 literature documents for textual user query, and COVID-19 question answering task predicts proper text fragments from text corpus as the answer of specific COVID-19 related questions. Based on transformer neural network, we provided solutions to implement the tasks on CORD-19 dataset, we display some examples to show the effectiveness of our proposed solutions.