Reproductive well-being is shaped by intersecting cultural, religious, gendered, and political contexts, yet current technologies often reflect narrow, Western-centric assumptions. In this literature review, we synthesize findings from 147 peer-reviewed papers published between 2015 and 2025 across HCI, CSCW and social computing, ICTD, digital and public health, and AI for well-being scholarship to map the evolving reproductive well-being landscape. We identify three thematic waves that focused on early access and education, cultural sensitivity and privacy, and AI integration with policy-aware design, and highlight how technologies support or constrain diverse reproductive experiences. Our analysis reveals critical gaps in inclusivity, with persistent exclusions of men and non-binary users, migrants, and users in the Global South. Additionally, we surfaced the significant absence of literature on the role of stakeholders (e.g., husband and family members, household maids and cleaning helping hands, midwife, etc.) in the reproductive well-being space. Drawing on the findings from the literature, we propose the ReWA framework to support reproductive well-being for all agendas through six design orientations associated with: location, culture, and history; polyvocality and agency; rationality, temporality, distributive roles, and methodology.
Scientific knowledge is growing rapidly, making it difficult to track progress and high-level conceptual links across broad disciplines. While tools like citation networks and search engines help retrieve related papers, they lack the abstraction needed to capture the needed to represent the density and structure of activity across subfields. We motivate SCIENCE HIERARCHOGRAPHY, the goal of organizing scientific literature into a high-quality hierarchical structure that spans multiple levels of abstraction -- from broad domains to specific studies. Such a representation can provide insights into which fields are well-explored and which are under-explored. To achieve this goal, we develop a hybrid approach that combines efficient embedding-based clustering with LLM-based prompting, striking a balance between scalability and semantic precision. Compared to LLM-heavy methods like iterative tree construction, our approach achieves superior quality-speed trade-offs. Our hierarchies capture different dimensions of research contributions, reflecting the interdisciplinary and multifaceted nature of modern science. We evaluate its utility by measuring how effectively an LLM-based agent can navigate the hierarchy to locate target papers. Results show that our method improves interpretability and offers an alternative pathway for exploring scientific literature beyond traditional search methods. Code, data and demo are available: https://github.com/JHU-CLSP/science-hierarchography
Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum
et al.
As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas. We introduce ScholarEval, a retrieval augmented evaluation framework that assesses research ideas based on two fundamental criteria: soundness - the empirical validity of proposed methods based on existing literature, and contribution - the degree of advancement made by the idea across different dimensions relative to prior research. To evaluate ScholarEval, we introduce ScholarIdeas, the first expert-annotated dataset of multi-domain research ideas and reviews, comprised of 117 ideas across four disciplines: artificial intelligence, neuroscience, biochemistry, and ecology. Our evaluation shows that ScholarEval achieves significantly higher coverage of points mentioned in the human expert annotated rubrics in ScholarIdeas compared to all baselines. Furthermore, ScholarEval is consistently preferred over our strongest baseline o4-mini-deep-research, a reasoning and search-enabled agentic system by OpenAI, in terms of evaluation actionability, depth, and evidence support. Our large-scale user study also shows that ScholarEval significantly outperforms deep research in literature engagement, idea refinement, and usefulness. We openly release our code, dataset, and ScholarEval tool for the community to use and build on.
Faced with the burgeoning volume of academic literature, researchers often need help with uncertain article quality and mismatches in term searches using traditional academic engines. We introduce IntellectSeeker, an innovative and personalized intelligent academic literature management platform to address these challenges. This platform integrates a Large Language Model (LLM)--based semantic enhancement bot with a sophisticated probability model to personalize and streamline literature searches. We adopted the GPT-3.5-turbo model to transform everyday language into professional academic terms across various scenarios using multiple rounds of few-shot learning. This adaptation mainly benefits academic newcomers, effectively bridging the gap between general inquiries and academic terminology. The probabilistic model intelligently filters academic articles to align closely with the specific interests of users, which are derived from explicit needs and behavioral patterns. Moreover, IntellectSeeker incorporates an advanced recommendation system and text compression tools. These features enable intelligent article recommendations based on user interactions and present search results through concise one-line summaries and innovative word cloud visualizations, significantly enhancing research efficiency and user experience. IntellectSeeker offers academic researchers a highly customizable literature management solution with exceptional search precision and matching capabilities. The code can be found here: https://github.com/LuckyBian/ISY5001
Surrayya Mobeen, Jann Cristobal, Shashank Singoji
et al.
The neural moving horizon estimator (NMHE) is a relatively new and powerful state estimator that combines the strengths of neural networks (NNs) and model-based state estimation techniques. Various approaches exist for constructing NMHEs, each with its unique advantages and limitations. However, a comprehensive literature review that consolidates existing knowledge, outlines design guidelines and highlights future research directions is currently lacking. This systematic literature review synthesizes the existing knowledge on NMHE, addressing the above knowledge gap. The paper (1) explains the fundamental principles of NMHE, (2) explores different NMHE architectures, discussing the pros and cons of each, (3) investigates the NN architectures used in NMHE, providing insights for future designs, (4) examines the real-time implementability of current approaches, offering recommendations for practical applications, and (5) discusses the current limitations of NMHE approaches and outlines directions for future research. These insights can significantly improve the design and application of NMHE, which is critical for enhancing state estimation in complex systems.
Literature review requires researchers to synthesize a large amount of information and is increasingly challenging as the scientific literature expands. In this work, we investigate the potential of LLMs for producing hierarchical organizations of scientific studies to assist researchers with literature review. We define hierarchical organizations as tree structures where nodes refer to topical categories and every node is linked to the studies assigned to that category. Our naive LLM-based pipeline for hierarchy generation from a set of studies produces promising yet imperfect hierarchies, motivating us to collect CHIME, an expert-curated dataset for this task focused on biomedicine. Given the challenging and time-consuming nature of building hierarchies from scratch, we use a human-in-the-loop process in which experts correct errors (both links between categories and study assignment) in LLM-generated hierarchies. CHIME contains 2,174 LLM-generated hierarchies covering 472 topics, and expert-corrected hierarchies for a subset of 100 topics. Expert corrections allow us to quantify LLM performance, and we find that while they are quite good at generating and organizing categories, their assignment of studies to categories could be improved. We attempt to train a corrector model with human feedback which improves study assignment by 12.6 F1 points. We release our dataset and models to encourage research on developing better assistive tools for literature review.
Chunwei Liu, Enrique Noriega-Atala, Adarsh Pyarelal
et al.
The global output of academic publications exceeds 5 million articles per year, making it difficult for humans to keep up with even a tiny fraction of scientific output. We need methods to navigate and interpret the artifacts -- texts, graphs, charts, code, models, and datasets -- that make up the literature. This paper evaluates various methods for extracting mathematical model variables from epidemiological studies, such as ``infection rate ($α$),'' ``recovery rate ($γ$),'' and ``mortality rate ($μ$).'' Variable extraction appears to be a basic task, but plays a pivotal role in recovering models from scientific literature. Once extracted, we can use these variables for automatic mathematical modeling, simulation, and replication of published results. We introduce a benchmark dataset comprising manually-annotated variable descriptions and variable values extracted from scientific papers. Based on this dataset, we present several baseline methods for variable extraction based on Large Language Models (LLMs) and rule-based information extraction systems. Our analysis shows that LLM-based solutions perform the best. Despite the incremental benefits of combining rule-based extraction outputs with LLMs, the leap in performance attributed to the transfer-learning and instruction-tuning capabilities of LLMs themselves is far more significant. This investigation demonstrates the potential of LLMs to enhance automatic comprehension of scientific artifacts and for automatic model recovery and simulation.
Research has shown that current informal extramural (out-of-class) activities can be an important predictor of second language (L2) vocabulary knowledge, but less is known about the relationship between language proficiency and activities earlier in life, which could have contributed to L2 acquisition. This exploratory study investigated Norwegian university students’ reported exposure to English through extramural activities at an early age and how this related to their current vocabulary size in L2 English. Participants (N = 40) completed an online survey comprising items from the Vocabulary Size Test (VST) and questions about the earliest extramural activity they felt made an important contribution to their knowledge of L2 English and the age at which they engaged in this activity. Participants’ mean English vocabulary size, as measured by the VST, was 11,246 words, and regression analysis found that the age of reported earliest extramural exposure was a significant predictor for L2 vocabulary size but that the current age of participants was not a significant predictor of VST scores. The results suggest that investigating early exposure to extramural input could be an important avenue for future research.
The ACL Anthology is an online repository that serves as a comprehensive collection of publications in the field of natural language processing (NLP) and computational linguistics (CL). This paper presents a tool called ``ACL Anthology Helper''. It automates the process of parsing and downloading papers along with their meta-information, which are then stored in a local MySQL database. This allows for efficient management of the local papers using a wide range of operations, including "where," "group," "order," and more. By providing over 20 operations, this tool significantly enhances the retrieval of literature based on specific conditions. Notably, this tool has been successfully utilised in writing a survey paper (Tang et al.,2022a). By introducing the ACL Anthology Helper, we aim to enhance researchers' ability to effectively access and organise literature from the ACL Anthology. This tool offers a convenient solution for researchers seeking to explore the ACL Anthology's vast collection of publications while allowing for more targeted and efficient literature retrieval.
Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects of this problem, failing to capture the real-world complexity and nuance required. Focusing on biomedicine, this work presents CARE -- a new IE dataset for the task of extracting clinical findings. We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes, which unifies phenomena challenging for current IE systems such as discontinuous entity spans, nested relations, variable arity n-ary relations and numeric results in a single schema. We collect extensive annotations for 700 abstracts from two sources: clinical trials and case reports. We also demonstrate the generalizability of our schema to the computer science and materials science domains. We benchmark state-of-the-art IE systems on CARE, showing that even models such as GPT4 struggle. We release our resources to advance research on extracting and aggregating literature findings.
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata's effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies.
Kenneth Løvold Rødseth, Rasmus Bøgh Holmen, Timo Kuosmanen
et al.
Abstract Comprehensive studies on the impact of market access on port efficiency are scarce, and the problem that market access indicators are potentially endogenous lacks treatment in maritime economics. This paper offers both theoretical and empirical advances to fill these research gaps. First, it pioneers in the use of Stochastic semi-Nonparametric Envelopment of Z variables Data for measuring port efficiency, and further develops the methodology for panel data and proposes an instrumental variable extension for dealing with endogenous market access indicators. Second, it advances the empirical port literature by developing a unique panel dataset on Norwegian container ports encompassing a comprehensive set of foreland and hinterland connectivity measures. Our comprehensive assessment suggests that the role of market access in determining port efficiency is uncertain.
Shipment of goods. Delivery of goods, Transportation and communications
Enrico Pollarolo, Tuula H. Skarstein, Ingunn Størksen
et al.
This article investigates the perspectives of Norwegian early childhood educators on mathematics and higher-order thinking. Thematic analysis of the connection between mathematics and children’s higher-order thinking skills was performed based on semi-structured interviews with ten educators in three different early childhood education and care (ECEC) centres. The findings suggest that educators, recognising mathematics as vital for ECEC, associate mathematics with problem-solving, an aspect of higher-order thinking skills highlighted in the research literature. The educators identified many opportunities for working with mathematics in daily activities, in accordance with the Norwegian tradition in recent years. Our results provide insights into how mathematics can support early childhood educators’ stimulation of higher-order thinking in the Norwegian ECEC context.
AbstractNorway has been a pioneer in the development and adoption of the Internet and mobile telephony technologies. Already from an early stage, Norway was involved in research, development, and testing of initiatives such as the ARPANET project and the Nordic Mobile Telephone (NMT) system. Today, access to the Internet and mobile technologies—including smartphones—is globally widespread. The major objective of this chapter is to describe how the market for the Internet and mobile telephony in Norway has evolved since its inception in the 1970s until today. The historical and current market structure of telecommunications is discussed. Moreover, the chapter investigates the role and significance of mobile virtual network operators (MVNOs). Finally, the chapter examines the regulations imposed by the Norwegian Communications Authority (Nkom) on dominant stakeholders in the Norwegian telecom market.
In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans. Since its inception, a large number of researchers throughout the globe have been pioneering the application of artificial intelligence in medicine. Although artificial intelligence may seem to be a 21st-century concept, Alan Turing pioneered the first foundation concept in the 1940s. Artificial intelligence in medicine has a huge variety of applications that researchers are continually exploring. The tremendous increase in computer and human resources has hastened progress in the 21st century, and it will continue to do so for many years to come. This review of the literature will highlight the emerging field of artificial intelligence in medicine and its current level of development.
This article gives an account of a teaching experience carried out from 2008 to 2021 at the university of Franche-Comt{é} as an answer to the ministerial command of proposing cross-disciplinary courses in the curricula. The goal of the experience was to develop simultaneously a discourse on mathematics and a discourse on literature, two independent discourses, but each filled with the gap between one domain and the other and mindful of the flashes of thought that are lightening from one to the other, witnesses of unity in their reasoning. -- Cet article rend compte d'une exp{é}rience d'enseignement men{é}e de 2008 {à} 2012 {à} l'universit{é} de Franche-Comt{é} en r{é}ponse {à} l'injonction minist{é}rielle de proposer des unit{é}s transversales dans les maquettes de dipl{ô}me. Le but de cette exp{é}rience a {é}t{é} de d{é}velopper {à} la fois un discours sur les math{é}matiques et un discours sur la litt{é}rature, deux discours ind{é}pendants, mais chacun rempli de l'{é}cart entre l'un des domaines et l'autre, et attentif aux {é}clairs de la pens{é}e qui jaillissent de l'un vers l'autre, t{é}moignages d'unit{é} dans la d{é}marche intellectuelle.
Elin Kjelle, Eivind Richter Andersen, Arne Magnus Krokeide
et al.
Abstract Background Inappropriate and wasteful use of health care resources is a common problem, constituting 10–34% of health services spending in the western world. Even though diagnostic imaging is vital for identifying correct diagnoses and administrating the right treatment, low-value imaging—in which the diagnostic test confers little to no clinical benefit—is common and contributes to inappropriate and wasteful use of health care resources. There is a lack of knowledge on the types and extent of low-value imaging. Accordingly, the objective of this study was to identify, characterize, and quantify the extent of low-value diagnostic imaging examinations for adults and children. Methods A scoping review of the published literature was performed. Medline-Ovid, Embase-Ovid, Scopus, and Cochrane Library were searched for studies published from 2010 to September 2020. The search strategy was built from medical subject headings (Mesh) for Diagnostic imaging/Radiology OR Health service misuse/Medical overuse OR Procedures and Techniques Utilization/Facilities and Services Utilization. Articles in English, German, Dutch, Swedish, Danish, or Norwegian were included. Results A total of 39,986 records were identified and, of these, 370 studies were included in the final synthesis. Eighty-four low-value imaging examinations were identified. Imaging of atraumatic pain, routine imaging in minor head injury, trauma, thrombosis, urolithiasis, after thoracic interventions, fracture follow-up and cancer staging/follow-up were the most frequently identified low-value imaging examinations. The proportion of low-value imaging varied between 2 and 100% inappropriate or unnecessary examinations. Conclusions A comprehensive list of identified low-value radiological examinations for both adults and children are presented. Future research should focus on reasons for low-value imaging utilization and interventions to reduce the use of low-value imaging internationally. Systematic review registration: PROSPERO: CRD42020208072.
Defining and bounding a case are recognized as especially important components in designing a case study. Discussions concerning the case study methodology and the relationship between theory and research in this domain has been featured in the literature for some time now. Yet, the process of identifying or constructing a case and the contribution of theory in this space seem neglected. This paper discusses how a case can be defined and bounded, and the role of the literature and theory in the process. Throughout the article, the author draws upon her experiences in the course of her PhD project vis-à-vis interests and power in Norwegian Svalbard politics. The article is divided into three parts. The first part considers how to define a case. The second part discusses how to bound a case. The third part digs deeper into the dilemmas of using the literature and theory in these processes. Although there may not be any clear solution to these dilemmas, the author finds that treating preliminary definitions and boundaries as sensitizing concepts can allow a researcher to find “the stuff” that pushes the study toward more interesting findings and theoretical innovations. However, neither methodology textbooks nor journal articles carry the solution to such dilemmas. Rather, the researcher’s own reflection specific to actual research can be a panacea.
ABSTRACT Can walking trails be understood not only as routes to history and heritage, but also as heritage in and of themselves? The paper explores the articulation of trails as a distinct landscape and mobility heritage, bridging the nature-culture divide and building on physical and intellectual movements over time. The authors aim to contribute to a better understanding of the geography of trails and trailscapes by analysing the emergence of the Swedish-Norwegian trail Finnskogleden. The trail is situated in the border region spanning the former county of Hedmark in present-day Innlandet County, south-eastern Norway, and Värmland County in mid-western Sweden, a forested area where Finnish-speaking immigrants settled from the 16th century to the early 20th century. Archives, literature, interviews, and field visits were used to analyse the emergence and governance of the trail. The main finding is the importance of continuous articulation work by local and regional stakeholders, through texts, maps, maintenance, and mobility. In conclusion, the Finn forest trailscape and its mobility heritage can be seen as an articulation of territory over time, a multilayered process drawing on various environing technologies, making the trail a transformative part of a trans-border political geography.