Academic Collaboration Research Articles

From lab to market: the financial impact of industry-university-research cooperation on Chinese pharma firms

PurposeThis study examines the impact of industry-university-research (IUR) cooperation on the financial performance of pharmaceutical enterprises in China. The current literature has primarily focused on the effects of IUR cooperation on innovative performance, often overlooking its financial implications. This research aims to address this gap by analyzing how such collaborations affect financial outcomes.Design/methodology/approachUsing a difference-in-differences (DID) approach, this study analyzes data from publicly listed Chinese pharmaceutical companies between 2011 and 2022. The analysis includes robustness checks and heterogeneity analysis to validate the findings.FindingsEmpirical results indicate that IUR cooperation significantly improves the financial performance of pharmaceutical enterprises. Firms engaged in IUR collaborations exhibit a notable improvement in return on assets (ROA) and return on equity (ROE). The research findings confirm that absorptive capacity (internal factors) and the external knowledge resource base (external factors) enhance the positive impact of IUR collaboration on the financial performance of pharmaceutical enterprises. Additionally, the heterogeneity analysis based on regional levels of intellectual property protection demonstrates a stronger positive influence of IUR cooperation on the financial performance of pharmaceutical enterprises in regions with greater levels of intellectual property protection.Originality/valueThis study contributes to the understanding of the financial benefits of IUR cooperation in the biopharmaceutical sector. By highlighting the significance of absorptive capacity, external knowledge resource base and regional intellectual property protection, this study offers valuable insights for policymakers and business leaders aiming to cultivate effective industry–academia collaborations.

A Game-Theoretic Assessment of Anti-Discrimination Measures in Scientific Collaboration

Abstract Rubin, Hannah, and Cailin O’Connor (2018. “Discrimination and Collaboration in Science.” Philosophy of Science 85 (3): 380–402) introduced a game-theoretic model to examine the dynamics of collaboration and discrimination in academia. They found that small minority groups face higher rates of discrimination in collaboration, despite moral and legal demands for equality. This paper extends their work by assessing the effectiveness of anti-discrimination measures, focusing on German legal provisions for discrimination in the workplace: internal complaints boards and training and organisational measures as mandated by the General Equal Treatment Act. My results show that internal boards are effective in preventing discrimination in initially fair (i.e. non-discriminatory) environments, but fall short in already discriminatory environments. In contrast, training and organisational measures significantly reduce discriminatory behaviour, even where awareness is low. This highlights the importance of proactive anti-discrimination measures.

Integrating Diverse Perspectives in Graduate Statistics Education Through Academic and Library Faculty Collaboration

Integrating Diverse Perspectives in Graduate Statistics Education Through Academic and Library Faculty Collaboration

A model to address the cold-start in peer recommendation by using k-means clustering and sentence embedding

In academia, research collaboration plays a vital role in enhancing the research quality and enriching the academic profile of the authors. Recommending appropriate collaborators from a vast scholarly database, particularly for newcomers, poses a challenging cold-start problem. This study addresses a cold-start problem in peer recommendation, considering a dynamic coauthorship graph as a network structure of academic collaborators. As the coauthorship graph is quite large and complex, an efficient indexing method is essential for speeding up the initial search of similar coauthors. The study introduces an efficient Global Inverted List (GIL) for indexing research areas and active authors in the coauthorship network. An attribute-based search and filtering mechanism is proposed to identify relevant collaborators, followed by the application of k-means clustering and doc2vec metrics to rank and select top recommendations. A cold user is associated with attributes that identify coauthors with similar research interests. For each attribute of the cold user, the model searches the associated authors from the GIL. Further, two filtering approaches are applied to refine the retrieved author list. The first ensures that the authors have a significant presence in the specified research areas, whereas the second one helps avoid recommending authors with only superficial connections to the cold user. The model creates a feature matrix of filtered authors using the publication features of authors. The k-means clustering applied to the feature matrix generates k clusters, among which the model chooses only those with seed nodes i.e. the clusters which are having seed nodes are selected for further process. Selected clusters are ranked using doc2vec metrics, with the top-ranked cluster providing the final recommendation. The model recommends the top L members of the selected cluster, where L is the length of the recommendations provided to the new user. Our extensive experiments show the efficacy of the proposed model.

Promotion of cooperation in a structured population with environmental feedbacks.

Cooperation is a representative altruistic behavior in which individuals contribute public goods to benefit their neighborhoods and even larger communities in social networks. The defective behavior is more likely to bring higher payoffs than the cooperative behavior, which makes the cooperative behavior hard to maintain and sustain. Many mechanisms were proposed to promote cooperation within a social dilemma, in which some recent studies introduced the impact of dynamically changing environments on players' payoffs and strategies in social-ecological systems, and evolutionary-ecological systems. However, degree heterogeneity, an important structural property of many real-world complex networks such as social networks, academic collaboration networks, and communication networks, is rarely explored and studied in such eco-evolutionary games. In this research, we propose a Public Goods Game model on social networks with environmental feedback and analyze how the environmental factor and network structure affect the evolution of cooperation. It is found that as the initial environmental factors and the cooperation-enhancement defection-degradation ratio increase, the steady cooperation level of the social network significantly increases, and the dynamic environment will eventually evolve into a high-return environment; On the other hand, even if the initial environmental benefit coefficient is high, when the cooperation-enhancement defection-degradation ratio is less than a threshold, the dynamic environment will gradually degrade into a low-return environment. The steady cooperation level of the social network first gradually increases as the network structure becomes more heterogeneous, but it will decrease once the heterogeneity of the social network exceeds a certain threshold.

Navigating The Digital Shift: A Bibliometric Analysis of Chatbot Applications in Education

In the ever-changing scenario of a technology-driven learning atmosphere, chatbots occupy a considerable role, taking up tasks in personalised learning and administrative support. This study uses a widespread bibliometric analysis of chatbots for educational purposes, engaging essential bibliographic data from Scopus and using the PRISMA flowchart approach for painstaking screening, exclusion and inclusion of studies in this field. Bibliometric tools like CiteSpace, VOSviewer, and Biblioshiny are utilised for this study to provide a multi-dimensional outlook on the effects and development of this field. The major findings of this study include an analysis of yearly scientific production about educational chatbots, identifying the most productive authors and synthesis of the most relevant sources and major cited documents globally. By analysing the influence of co-citation of cited authors and timezone views of cited journals, this study goes deep into the particulars of academic collaborations. This study analyzes the co-occurrence of keywords and those with the highest citation bursts to comprehend the intellectual structure of the topic’s research field, giving an outlook into the focus areas and evolving trends. This study also undertakes to find practical implications and relevant research gaps. These insights identify the features of chatbot research and suggest future directions in practical application and scholarly inquiry. For effective incorporation and evolution of chatbots in the educational scenario, academicians, technologists, and educators can use this bibliometric analysis as an essential resource.

Reforming a Broken Narrative on Student Affairs and Academic Affairs Collaboration: One Program’s Partnership Model to Foster Summer Bridge Program Success

ABSTRACT Student affairs and faculty partnerships contribute richly to learners’ academic and personal development, particularly with respect to the holistic care of first-generation college students and other marginalized learners. Yet, we still hear the broken narrative that problematizes these partnerships and even speaks to the work of student affairs as a shadow curriculum compromising learning and inquiry. In this article, we offer a counter-story that centers the power and possibilities of student affairs and faculty collaboration in the context of one First-Gen+ Center’s mission to embrace faculty in support and improvement of a summer bridge program serving first-generation college students.

Application of machine learning in acute upper gastrointestinal bleeding: bibliometric analysis.

In the past decade, the application of machine learning (ML) in the clinical management of acute upper gastrointestinal bleeding (AUGIB) has received much attention and has become a hot research topic. However, no scientometric report has systematically summarized and outlined the research progress in this field. This study aims to utilize bibliometric analysis methods to delve into the applications of machine learning in AUGIB and the collaborative network behind it over the past decade. Through a thorough analysis of relevant literature, we uncover the research trends and collaboration patterns in this field, which can provide valuable references and insights for further in-depth exploration in the same field. Using the Web of Science (WOS) as the data source, this study explores academic development in a specific field from December 2013 to December 2023. The search strategy included terms related to "Machine Learning" and "Acute Upper Gastrointestinal Bleeding". Only original articles in English focusing on ML in AUGIB were included. The analysis of downloaded literature with Citespace software, including keyword co-occurrence, author collaboration networks, and citation relationship networks, reveals academic dynamics, research hotspots, and collaboration trends. After sorting and compiling, we have collected 73 academic papers written by 217 authors from 133 institutions in 29 countries worldwide. Among them, China and AM J GASTROENTEROL have made significant contributions in this field, providing many high-quality research achievements. The study found that these papers mainly focus on three core research hotspots: deepening clinical consensus, precise analysis of medical images, and optimization of data integration and decision support systems. This study summarizes the latest advancements in the application of machine learning to AUGIB research. Through bibliometric analysis and network visualization, it reveals emerging trends, origins, leading institutions, and hot topics in this field. While this area has already demonstrated significant potential in medical artificial intelligence, our findings will provide valuable insights for future research directions and clinical practices.

ECO_CARE Fieldtrip on the Rio Negro (municipality of Novo Airão, Comunidade Tiririca) and academic collaboration with indigenous women, traditional people and Master’s students

This report summarises the activities undertaken by the ECOCARE team during the week of November 3 to November 4, focusing on fieldwork on the Rio Negro, specifically in the municipality of Novo Airão and the Comunidade Tiririca. Additionally, the team organised a meeting with the indigenous women of the Amarn organization and an academic activity on storytelling and emotional literacy with the Master’s students of the Postgraduate Program in Law at the UFAM, emphasizing a hands-on approach to learning and mutual exchange of knowledge.The activities were part of a broader initiative to foster meaningful partnerships and enhance educational opportunities through direct interaction with indigenous communities and academic partners. This initiative aligns with our ongoing commitment to support indigenous scholarship and integrate it within academic frameworks, thereby enriching the educational experiences of all participants.

Internationalisation in Higher Education: Post-Covid Trends of Students’ Mobility (Case Study EUROMED Countries)

Purpose: This article examines trends in the internationalisation of higher education within the Euro-Mediterranean (EUROMED) region, with a particular focus on student mobility as a driving force for fostering academic collaboration across borders. Study design/methodology/approach: The research uses data from UNESCO to analyse student movement patterns in 23 Euro-Mediterranean countries. It considers key factors influencing these trends, including economic incentives, geopolitical stability, and cultural linkages. It also assesses the impact of the COVID-19 pandemic on international student mobility and the adaptation of physical, virtual, and blended mobility formats to new realities. Findings: The findings highlight that while physical mobility remains essential, the rise of virtual and blended mobility options post-pandemic has introduced more inclusive and accessible student pathways. This shift enhances the resilience and adaptability of internationalisation frameworks within the EUROMED region, enabling a more robust response to the fluctuating global conditions. Originality/value: This study provides a comprehensive look at the unique cultural, geopolitical, and educational context of the EUROMED region. It underscores the importance of innovative mobility models in creating a more inclusive internationalisation approach that can withstand external challenges, particularly in a post-pandemic landscape.

Quandaries of Trying to Do Good-The Adequacy of the WHO FENSA Regulations.

The World Health Organization's (WHO) Framework of Engagement with Non-State Actors (FENSA), established in 2016, is designed to enhance transparency, impartiality, and conflict-of-interest safeguards by setting rigorous guidelines for WHO's interactions with private entities, particularly those in high-risk industries such as tobacco, alcohol, and arms. This paper briefly reviews the implementation and impact of FENSA, observing that, despite these safeguards, its application in academic contexts poses specific challenges. Universities, often reliant on diverse funding sources, may find the rules restrictive and misaligned with independent funding needs. The creation of the WHO Foundation in 2020 further complicates this landscape by enabling engagements with previously restricted private sector entities through an "arm's length" model. The authors advocate for a reassessment of FENSA to resolve inconsistencies and support essential academic collaborations, while upholding WHO's commitment to ethical standards.

Matvei Nikolaevich Khangalov: an intellectual biography of a distinguished ethnographer

In this article, we attempt to present an intellectual biography of the prominent Buryat ethnographer and public educator Matvei Khangalov (1858–1918). He remains an important figure in the Buryat public community, yet little attention has been paid to his life and work in anthropological studies. Drawing on archival and literary sources, as well as various recollections of his contemporaries, we explore his family background, pedagogical activities, public engagement, scholarly legacy and publications, tracing the chronology of his academic career and the scope of his field research and academic collaborations. The new findings shed light on the historical circumstances, shaped by the specific development of Mongol studies and ethnography within the Russian Empire, that provided the scholar with the opportunity to fully realize his creative potential.

Examining research systems and models for local government: a systematic review

Background: Local authorities (LA) are key in improving population health, and LA public health decision makers need support from appropriately organised research capacity; however, few models of LA research systems are known to exist. Aims and objectives: To explore potential and existing models of LA-based research systems. Methods: This mapping review and time-constrained systematic review synthesises conceptual and empirical literature from 12 health and social science databases, grey literature and reference/citation tracking. Three reviewers screened titles, abstracts and full texts of retrieved records, and extracted key data from included papers. Evidence was synthesised based on characteristics of research systems and quality-assessed for relevance, rigour and richness. Findings: Nine models were examined in depth. From these, we developed a typology of research systems. Few models were specifically designed for LA research activity; as a Whole System approach, the Local Authority Champions of Research model offers a potential blueprint. Useful lessons may be learned from UK Collaborations for Leadership in Applied Health Research, Academic Collaborative Centres in the Netherlands, local Research and Development units in Sweden, and generic University-Community partnerships. Discussion and conclusions: An optimal research system requires the coexistence of multiple systems including Centre, Partnership, Collaboration, Network and Community types. The review is UK-focused, but the models appear to have wider relevance. Our classification offers those planning an LA research system the opportunity to choose an approach that meets their requirements and resources. A Whole System approach is optimal, with egalitarian input from the LA and academia.

Academic collaboration

No abstract available.

Academic collaboration recommendation based on graph neural network and multi-attribute embedding

Academic collaboration recommendation (ACR) can help researchers find potential partners for research and thus promote academic innovation. Recent works mostly use graph learning-based methods to explore various ways of combining node information with topology, which consists of multiple steps, including network construction, node feature extraction, network representation learning and link prediction. One limitation is that they only conduct research on co-authorship networks and ignore citation relationship between publications. Besides, they tend to use attributes from a single dimension of researchers and do not take attributes of researchers from multiple dimensions into consideration at the same time. To address the above issues, we present the multi-dimensional attributes enhanced heterogeneous (MAH) network representation learning method, which constructs heterogeneous networks with both co-authorship and citation information and makes use of multi-dimensional attributes. Three research questions are addressed in this work: (RQ1) Is our proposed method effective on academic collaboration recommendation compared with existing state-of-the-art methods? (RQ2) Does incorporating citation information into co-authorship network help improve the performance of academic collaboration recommendation? (RQ3) How does fusing multi-dimensional attributes affect the performance of academic collaboration recommendation? A publicly available real-world data set is used in our experiments. The superior performance of MAH compared with baseline methods demonstrates that the proposed multi-dimensional feature-based researcher profile can enrich node information in academic network and effective researcher representations can be learned by applying graph representation learning methods on the network.

Empowering youths through citizen science to address health inequalities in adolescence

Abstract Citizen Science (CS) stands as a beacon of hope in the fight against inequalities in adolescent health, revolutionizing scientific research by involving individuals from diverse backgrounds. Unlike traditional methods, CS transcends academic boundaries, fostering inclusivity and collaboration irrespective of age, nationality, or social status. This approach has gained momentum in adolescent health research, empowering young people as co-researchers from project inception, thereby reducing disparities in health knowledge and access. Studies demonstrate its efficacy in enhancing empowerment and health literacy among participants. YouthChronic is an example of how CS can mitigate health inequalities among adolescents. The project ensures active participation in every research stage by engaging adolescents with chronic conditions as citizen scientists. Open-ended questions facilitate detailed responses, while participation in analysis and dissemination enhances their research involvement and sense of ownership. Through initiatives like YouthChronic, CS is reshaping the research landscape, making it more relevant and impactful to bridge the inequality gap. Young Citizen Scientists’ views on their participation: “As part of YouthChronic, we felt like our experiences with chronic conditions were finally being heard. The researchers were cool-they listened to us, let us be flexible with our participation, and even taught us some research skills. Learning from other peers’ experiences made us feel less alone and gave us new insights. But it’s frustrating that we’re limited by resources. We have big ideas, like running an advocacy campaign, but we need funding to make them happen”. In conclusion, by actively involving adolescents in the scientific process through CS initiatives like YouthChronic, we can reduce inequalities in adolescent health, empower marginalized voices, and pave the way for a brighter, more inclusive future in scientific exploration and discovery.

Single Mothers Association of Kenya (SMAK) and the Fight to Stay Put on the Ziwani Estate in Nairobi

This Reflection discusses the fight to stay put on the Ziwani Estate in Nairobi, in the face of the redevelopment (gentrification) of one of the first East African public housing projects developed in Kenya under colonial rule. The Single Mothers Association of Kenya (SMAK), a community-based organization founded on the Ziwani Estate, is leading the fight for/with Ziwani residents. SMAK is focused on land and housing rights, capacity building for the community on these rights, advocating against illegal eviction, and fighting against gentrification, densification, and homelessness. Under immediate threat SMAK found local and global allies in academia, civil society, and in UN bodies. They have had some important wins on the preservation and conservation of Ziwani historic places and people based on unexpected academic collaborations and on their own activism which they believe requires civic education, domestic and international lobbying, and possibly legal struggle.

Academic publishing as an exercise of leadership: evidence from doctoral students in the United Kingdom and Hong Kong

ABSTRACT This study inquires into the experiences of 15 doctoral students from universities in the United Kingdom and Hong Kong to examine the factors that contribute to publishing first-authored academic papers and explores their self-perceptions of their scholarly identities. Using Leadership for Learning as a theoretical framework, the research findings show that successful academic publishing is underpinned by powerful learning experiences, the sharing of leadership and a shared sense of accountability. The study demonstrates that student agency, which links leadership and learning, is manifested through purposeful action, relational engagement and self-transformation. The research results acknowledge the unequal power dynamics in different research collaborations and reveal that doctoral students’ identity development is interlinked with a passion for research, strong learning experiences, the way that others position themselves and autonomy. The study highlights the need for robust learning experiences, tailored support for academic publishing and leadership development opportunities that facilitate academic collaborations.

Bibliometric Insights into the "Kathmandu School of Law Review"

This bibliometric study analyzes the academic reach, thematic focus, and collaboration trends of the Kathmandu School of Law Review (KSLR). Through keyword analysis, the research highlights recurring themes such as human rights, governance, policy-making, and development, reflecting a multidisciplinary approach to legal scholarship. Visualizations of co-authorship networks reveal limited collaboration among scholars, suggesting opportunities for the journal to foster joint publications and interdisciplinary partnerships. Citation network analysis indicates a niche area of research with a few central authors like Puja Mitra and Yubaraj Sangroula, while the low overall citation count points to the need for broader academic engagement and visibility. Bibliographic coupling maps show emerging research clusters, indicating the potential for KSLR to build authority in key areas such as South Asian legal systems and international human rights. The study concludes with recommendations for KSLR to increase global outreach, promote interdisciplinary studies, and enhance academic collaboration to strengthen its impact within and beyond the region.

Learning accurate neighborhood- and self-information for higher-order relation prediction in Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are commonly employed to model complex real-world scenarios with diverse node and edge types. However, due to constraints in data collection and processing, constructed networks often lack certain relations. Consequently, various methods have emerged, particularly recently, leveraging heterogeneous graph neural networks (HGNNs) to predict missing relations. Nevertheless, these methods primarily focus on pairwise relations between two nodes. Real-world interactions, however, often involve multiple nodes and diverse types, extending beyond simple pairwise relations. For instance, academic collaboration networks may entail interactions among authors, papers, and conferences simultaneously. Despite their prevalence, higher-order relations are often overlooked. While HGNNs are effective at learning network structures, they may suffer from over-smoothing, resulting in similar representations for nodes and their neighbors. The learned inaccurate proximity among nodes impedes the discernment of higher-order relations. Furthermore, observed edges among a target group of nodes can provide valuable evidence for predicting higher-order relations. To address these challenges, we propose a novel model called Accurate Neighborhood- and Self-information Enhanced Heterogeneous Graph Neural Network (ANSE-HGN). Building upon HGNNs to encode network structure and attributes, we introduce a relation-based neighborhood encoder to capture information within multi-hop neighborhoods in heterogeneous higher-order relations. This enables the calculation of accurate proximity among target groups of nodes, thereby enhancing prediction accuracy. Additionally, we leverage self-information from observed higher-order relations as an auxiliary loss to reinforce the learning process. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed method in higher-order relation prediction tasks.