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Performance-based research funding and gender diversity in research: evidence from UK universities
Get PDFThis study provides causal evidence on how performance-based research funding affects gender diversity, using the UK’s transition from the Research Assessment Exercise to the Research Excellence Framework in 2009 as a natural experiment. Using difference-in-differences estimation, we compare twenty-four Russell Group UK universities with twenty-three matched US research-intensive universities from 2001 to 2021. Results demonstrate that performance-based funding increased female participation in collaborative research by 10.3 percentage points (0.90 standard deviations). Citation analysis reveals that increased female participation coincided with higher research impact, with treated papers receiving 4.79 more citations on average. Our findings suggest that performance-based research funding effectively promotes gender diversity while maintaining research quality. It is important to note, however, that increased female participation alone does not resolve or address the persistent gender pay disparities in UK academia
Semantic similarity in community forum questions: case study on Quora dataset
Get PDFDuplicate questions on crowd-sourced question and answer websites such as Quora create redundancy and make information retrieval inefficient. This research conducts a systematic comparative analysis of machine learning and deep learning models for detecting semantic similarity in questions. Using the Quora Question Pairs dataset, we evaluate a spectrum of models: a classical TF-IDF baseline, feature-engineered Random Forest and XGBoost, a Siamese Manhattan LSTM (MaLSTM), and a fine-tuned BERT model. The study reveals a clear performance hierarchy. A key finding is that classical models with a limited set of hand-crafted linguistic features underperformed the simple TF-IDF baseline. While the MaLSTM network showed moderate improvement, the fine-tuned BERT model was unequivocally superior, achieving a statistically significant accuracy of 86.26%. This highlights the critical role of deep contextual embeddings for this task. However, BERT’s state-of-the-art performance comes at a significant computational cost, revealing a crucial trade-off between accuracy and resource efficiency. These findings provide a pragmatic guide for designing effective and scalable duplicate question detection systems
Grow through what you go through: a multiple-case study of competitive bodybuilders’ experiences of learning to manage the demands of their engagement in the sport
Get PDFTo date, most research amongst competitive bodybuilders has focused on highlighting the demands of competitive bodybuilding, competitors’ emotional and behavioural responses to these demands, and the subsequent psychosocial outcomes, with limited attention to the process of coping. The current study aims to address this gap in the literature by providing insight into how competitors learn to manage and cope with the demands of their sport. Using a multiple-case study design, five high-profile competitive bodybuilders (with over 211,000 Instagram followers and 82,000 YouTube subscribers combined) engaged in semi-structured interviews and provided Instagram and personal journal data. Using reflexive thematic analysis, three overarching themes were constructed: (a) learning by trial and error, (b) understanding the self, the substances and the process, and (c) flexible guiding priorities. These findings have implications for informing future harm reduction initiatives amongst competitive bodybuilders (e.g. accelerating the experiential learning process), as well as enhancing social support for competitors (e.g. encouraging communal coping). Furthermore, this study illustrates the value of combining traditional methods (e.g. semi-structured interviews, journals) and social media data (e.g. Instagram posts, vlog-style videos) when conducting qualitative case studies in order to provide a comprehensive understanding of the phenomenon of interest
Influence of cultural beliefs and parental feeding practices on obesity among primary schoolchildren aged 6-12 in Ghana: a qualitative study
Get PDFAbstractChildhood obesity has become a global public health challenge and as such has attracted worldwide attention due to its negative impact on children’s health. Despite its diverse determinants, there is a paucity of information on cultural beliefs and parental feeding practices related to childhood obesity in Ghana. This study aimed to explore the influence of cultural beliefs and parental feeding practices on obesity among schoolchildren in Ghana.Background: Childhood obesity is a global public health concern, drawing widespread attention for its negative impact on children’s health. While the determinants are multifaceted, limited information exists on the impact of cultural beliefs and parental feeding practices in the context of childhood obesity in Ghana. The primary objective of this exploratory study was to investigate the influence of cultural beliefs and parental feeding practices on obesity among schoolchildren in Ghana.Method: Data for the study were collected through an online interview and focus group discussion from a purposively sampled 60 respondents. An audio recording device was used to compile information shared with respondents during the interview and focus group discussion, both held remotely over the internet. Following Braun and Clarke’s procedure for analysing data, audio-recorded information was transcribed verbatim using Microsoft Word. Vital information to address research questions was assigned codes for collation. Similar codes were collated to form subthemes and major themes which aligned with the Attride-Stirling transcription approach of thematic analysis.Findings: Four themes emerged from data analysis: parental beliefs and perception of weight and feeding practices; evolving dietary practices; the impact of westernisation and socioeconomic status; and lifestyle at home and obesogenic environments. The cultural inclination towards considering obesity as a sign of a ‘well-fed child’ was evident, and traditional feeding practices were found inadequate, necessitating supplementation with modern approaches. Additionally, factors such as digital media, limited playing space and sedentary behaviours facilitated by transportation to school and easy access to electronic devices contributed to obesity among schoolchildren.Conclusion: While parents actively promoted mixed food diets, this often conflicted with nutritional needs. Parents also inadvertently encouraged sedentary behaviours hindering physical activity and contributing to weight gain among children. The study highlighted the challenges posed by cultural beliefs on body image and modern influences, necessitating a comprehensive understanding to formulate effective interventions to address childhood obesity in the Ghanaian context
Enhancing customer segmentation through factor analysis of mixed data (FAMD)-based approach using K-Means and hierarchical clustering algorithms
Get PDFIn today’s data-driven business landscape, effective customer segmentation is crucial for enhancing engagement, loyalty, and profitability. Traditional clustering methods often struggle with datasets containing both numerical and categorical variables, leading to suboptimal segmentation. This study addresses this limitation by introducing a novel application of Factor Analysis of Mixed Data (FAMD) for dimensionality reduction, integrated with K-means and Agglomerative Clustering for robust customer segmentation. While FAMD is not new in data analytics, its potential in customer segmentation has been underexplored. This research bridges that gap by demonstrating how FAMD can harmonize mixed data types, preserving structural relationships that conventional methods overlook. The proposed methodology was tested on a Kaggle-sourced retail dataset comprising 3900 customers, with preprocessing steps including correlation ratio filtering (η ≥ 0.03), standardization, and encoding. FAMD reduced the feature space to three principal components, capturing 81.46% of the variance, which facilitated clearer segmentation. Comparative clustering analysis showed that Agglomerative Clustering (Silhouette Score: 0.52) outperformed K-means (0.51) at k = 4, revealing distinct customer segments such as seasonal shoppers and high spenders. Practical implications include the development of targeted marketing strategies, validated through heatmap visualizations and cluster profiling. This study not only underscores the suitability of FAMD for customer segmentation but also sets the stage for more nuanced marketing analytics driven by mixed-data methodologies
Estimating the replicability of Sports and Exercise Science research
Get PDFBackgroundThe replicability of sports and exercise research has not been assessed previously despite concerns about scientific practices within the field.AimThis study aims to provide an initial estimate of the replicability of applied sports and exercise science research published in quartile 1 journals (SCImago journal ranking for 2019 in the Sports Science subject category; www.scimagojr.com) between 2016 and 2021.MethodsA formalised selection protocol for this replication project was previously published. Voluntary collaborators were recruited, and studies were allocated in a stratified and randomised manner on the basis of equipment and expertise. Original authors were contacted to provide deidentified raw data, to review preregistrations and to provide methodological clarifications. A multiple inferential strategy was employed to analyse the replication data. The same analysis (i.e. F test or t test) was used to determine whether the replication effect size was statistically significant and in the same direction as the original effect size. Z-tests were used to determine whether the original and replication effect size estimates were compatible or significantly different in magnitude.ResultsIn total, 25 replication studies were included for analysis. Of the 25, 10 replications used paired t tests, 1 used an independent t test and 14 used an analysis of variance (ANOVA) for the statistical analyses. In all, 7 (28%) studies demonstrated robust replicability, meeting all three validation criteria: achieving statistical significance (p < 0.05) in the same direction as the original study and showing compatible effect size magnitudes as per the Z test (p > 0.05).ConclusionThere was a substantial decrease in the published effect size estimate magnitudes when replicated; therefore, sports and exercise science researchers should consider effect size uncertainty when conducting subsequent power analyses. Additionally, there were many barriers to conducting the replication studies, e.g., original author communication and poor data and reporting transparency
A design-based research approach to understanding women's journey into executive leadership through higher education
No full textThis paper examines the complex pathways women navigate toward executive leadership positions, with particular focus on the role of higher education in shaping aspirations, the influence of role models and mentorship, and the significance of organisational culture in facilitating or constraining women’s leadership development. Drawing from a comprehensive Design-Based Research (DBR) study that developed and implemented an innovative MBA Women’s Leadership course, this research employs Interpretative Phenomenological Analysis (IPA) and Bakhtin’s dialogic framework to understand how women construct and negotiate their leadership identities within institutional contexts. The study addresses three critical research questions through a mixed-methods approach involving 45participants across two iterative cycles of course development and implementation. Quantitative analysis revealed statistically significant improvements across all leadership dimensions (p<0.001), with effect sizes ranging from 0.89 to 1.45, indicating large to very large practical significance. Qualitative findings demonstrate that women continue to face systemic barriers including gender stereotypes (73.3% of participants), limited role model availability (84.4%), and work-life integration challenges (77.8%).Key findings demonstrate that when leadership development is specifically tailored to women's lived experiences and challenges, it can lead to enhanced confidence (Cohen’s d=1.24), deeper self-understanding, and stronger preparation for leadership roles. Six-month follow-up data revealed that 42.9% of participants received promotions or advancement, 59.5% gained new leadership opportunities, and 83.3% pursued additional professional development. The study contributes both theoretical insights into leadership identity formation and practical strategies for creating more inclusive educational and organisational environments. The research highlights the critical importance of intersectional approaches that recognise the compounded barriers faced by women from diverse backgrounds, with women of colour reporting 15-22% higher barrier levels across most categories compared to white women. These findings have significant implications for educational institutions, organisational leaders, and policymakers committed to advancing gender equity in leadership positions
Women’s leadership development through higher education
No full textThis study investigates how higher education shapes women’s pathways to executive leadership positions, examining the role of educational interventions, role models, and organisational culture in supporting women’s leadership development and career advancement in contemporary professional contexts. A design-based research (DBR) methodology, integrated with Interpretative Phenomenological Analysis, was employed. Data collection included semi-structured interviews, participant journals, focus groups, and validated leadership assessments. An MBA Women’s Leadership course was developed and implemented across two iterative cycles with 45 participants. Statistically significant improvements were observed across all leadership dimensions (p&lt;0.001, Cohen’s d=0.89-1.45). At six-month follow-up, 42.9% of participants received promotions, 59.5% gained new leadership opportunities, and 83.3% pursued additional professional development activities. Findings inform the design of leadership development programmes in higher education institutions, organisational diversity and inclusion initiatives, policy development for gender equity in leadership, and evidence-based approaches to women’s professional advancement in educational and corporate contexts. This research provides the first systematic integration of Design-Based Research with Interpretative Phenomenological Analysis for women’s leadership development, offering both theoretical insights into leadership identity formation and practical intervention models for educational institutions
Art encounters in the nuclear age: radiant objects
No full textThis collection aims to expand discourse around nuclear power and bear witness to the strangeness of living in the nuclear age and to contribute to a culture of care for nuclear subjects. Particularly the collection seeks to direct attention to the marginalised voices of those who have experienced nuclear traumas more directly. The book can be read as a kind of inventory of objects, of things close-to-hand, that can allow us to sense the magnitude and complexity of issues arising from nuclear technologies. Amidst prevailing eco-anxiety, these essays ask us to sit with these experiences, to ‘hold’ these objects and to come closer to understanding our nuclear heritage. Topics include: the legacies of nuclear bombs; the nuclear archive and 'noise prints', nuclear decommissioning; the past and present of nuclear bunkers; and art in Fukushima
An interpretable and generalizable machine learning model for predicting asthma outcomes: integrating AutoML and explainable AI techniques
No full textAsthma remains a prevalent chronic condition, impacting millions globally and presenting significant clinical and economic challenges. This study develops a predictive model for asthma outcomes, leveraging automated machine learning (AutoML) and explainable AI (XAI) to balance high predictive accuracy with interpretability. Using a comprehensive dataset of demographic, clinical, and respiratory function data, we employed AutoGluon to automate model selection, optimization, and ensembling, resulting in a model with 98.99% accuracy and a 0.9996 ROC-AUC score. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) were applied to provide both global and local interpretability, ensuring that clinicians can trust and understand model predictions. Additionally, counterfactual analysis enabled hypothetical scenario exploration, supporting personalized asthma management by allowing clinicians to assess potential interventions for individual patient risk profiles. To facilitate clinical adoption, a Streamlit v1.41.0 application was developed for real-time access to predictions and interpretability. This study addresses key gaps in asthma prediction, notably in model transparency and generalizability, while providing a practical tool for enhancing personalized care. Future research could expand the validation across diverse patient populations to reinforce the model’s robustness in broader clinical environments
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