Diverse Representations Research Articles

Developing a trustworthy national health data space: public needs and policy implications

Abstract Background The ongoing development of a national health data space in Switzerland presents an opportunity to transform health delivery and care by enabling large-scale secondary health research. Central to this development is the Health2030 strategy, which calls for the establishment of public trust in the reliability and security of data collection, storage, and exchange. Methods We conducted four focus groups across the German, French and Italian-speaking regions of Switzerland to inform the development of the Swiss health data space that fosters public trust. To inform Swiss policy, we held a panel discussion with patient experts and healthcare professionals to translate the focus group findings into governance and public communication requirements. We synthesized our findings using a thematic analysis approach and proposed a guiding health policy strategy. Results Citizens perceive the health data space as trustworthy conditional on the delivery of its promised higher levels of transparency, security, and lower risk compared to other data sharing initiatives. To ensure its trustworthiness, citizens call for the provision of value-based consent to control data access based on the actors involved and research purposes, alongside implementing accountability measures for data misuse. Governance framework requirements entail establishing an advisory group for the health data space with sufficient representation of the Swiss population, including members of the public, patient advisory groups, and researchers. Public communication requirements call for ongoing monitoring of public opinion on health data space, alongside establishing public engagement initiatives, such as public fora, to inform ongoing progress. Conclusions Policy efforts should prioritize the meaningful involvement of relevant stakeholders throughout all stages of the health data space development to foster trust. Central to these efforts is balancing public expectations with their practical implementation. Key messages • For the health data space to be trustworthy, citizens require transparency on their data use and the security measures taken, value-based consent for data access, and accountability measures. • Diverse representation in governance and continuous public involvement are key policy measures to build public trust in the development of national health data spaces.

A knowledge-guided process planning approach with reinforcement learning

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods.

Ensemble-Enhanced Semi-Supervised Learning with Optimized Graph Construction for High-Dimensional Data.

Graph-based methods have demonstrated exceptional performance in semi-supervised classification. However, existing graph-based methods typically construct either a predefined graph in the original space or an adaptive graph within the output space, which often limits their ability to fully utilize prior information and capture the optimal intrinsic data distribution, particularly in high-dimensional data with abundant redundant and noisy features. This paper introduces a novel approach: Semi-Supervised Classification with Optimized Graph Construction (SSC-OGC). SSC-OGC leverages both predefined and adaptive graphs to explore intrinsic data distribution and effectively employ prior information. Additionally, a graph constraint regularization term (GCR) and a collaborative constraint regularization term (CCR) are incorporated to further enhance the quality of the adaptive graph structure and the learned subspace, respectively. To eliminate the negative effect of constructing a predefined graph in the original data space, we further propose a Hybrid Subspace Ensemble-enhanced framework based on the proposed Optimized Graph Construction method (HSE-OGC). Specifically, we construct multiple hybrid subspaces, which consist of meticulously chosen features from the original data to achieve high-quality and diverse space representations. Then, HSE-OGC constructs multiple predefined graphs within hybrid subspaces and trains multiple SSC-OGC classifiers to complement each other, significantly improving the overall performance. Experimental results conducted on various high-dimensional datasets demonstrate that HSE-OGC exhibits outstanding performance.

Enhancing Equity in Clinical Research: A Multifaceted Proposal for Spondyloarthritis.

Clinical research advances medical knowledge and improves healthcare outcomes. However, disparities in research participation hinder progress. The Unmet Research Needs in Spondyloarthritis Conference IV highlighted critical insights and strategies to enhance equity in clinical research. Talks focused on engaging underrepresented communities and addressing disparities in rheumatic diseases, particularly spondyloarthritis (SpA), to ensure research results are generalizable and inclusive. Disparities in SpA management, such as greater back pain severity among Black and Hispanic Americans and sex-based differences in pain management, emphasize the need for equitable research. Dr. Elizabeth Ferucci discussed the racial disparities in rheumatologic care, highlighting the importance of early access to rheumatologists and culturally informed primary care to improve outcomes. Dr. Hani El-Gabalawy's talk on engaging Indigenous communities stressed the importance of community consent and reciprocal benefits. Dr. Sarfaraz Hasni's presentation on mitigating disparities in research participation underscored the need for inclusive practices and strategies to promote diverse representation. Finally, Dr. Edith Williams emphasized institutional approaches to fostering equity, including diverse recruitment practices and institutional review board alignment with diversity priorities. Strategies to enhance equity in clinical research include community engagement, addressing logistical barriers to participation, and ensuring diverse research teams. These approaches can dismantle barriers for underrepresented communities, making research more accessible and reflective of the broader population. The SpA research community must commit to creating structures that foster inclusivity, ensuring medical advancements benefit all populations, especially historically underrepresented groups. The principles and strategies proposed serve as a roadmap for achieving equity in SpA research.

Enhanced multinomial logistic regression analysis of determinants influencing technical and vocational education and training (TVET) choices among Ghanaian youth: Implications for policy development

In Ghana, youth unemployment remains significant challenges, with technical and vocational education and training (TVET) emerging as a potential solution to equip young people with practical skills for the job market. However, the uptake of TVET programmes among Ghanaian youth remains low, particularly among females. This study therefore explores the determinants that influence TVET choices among Ghanaian youth, with the goal of informing policy development to enhance participation in vocational education. Applying an enhanced multinomial logistic regression (MLR) model, this research examines the influence of socio-economic, demographic, and attitudinal factors on career decisions. The enhanced model accounts for class imbalances in the dataset and improves classification accuracy, making it a robust tool for understanding the drivers behind TVET choices. A sample of 1600 Ghanaian youth engaged in vocational careers was used, ensuring diverse representation of the population. Key findings reveal that males are approximately three times more likely to choose TVET programs than females, despite females making up 50.13% of Ghana’s population. Specific determinants influencing TVET choices include financial constraints, parental influence, peer influence, teacher influence, self-motivation, and vocational limitations. In regions with limited vocational options, youth often pursue careers based on availability rather than preference, which highlights a gap in vocational opportunities. Parental and teacher influences were found to play a dominant role in steering youth towards specific careers. The study concludes with recommendations for policymakers, instructors, and stakeholders to increase the accessibility, relevance, and quality of TVET programmes to meet the socio-economic needs of Ghanaian youth.

Size bias in the documentation of marine biodiversity

Biodiversity exploration is incomplete, with a substantial gap between described and undiscovered species. Biases occur in these taxonomic descriptions, favoring easily observable and common species, leading to a skewed representation of ecological diversity, particularly in marine environments where deep‐sea life and smaller invertebrate groups remain underexplored. Here, we examine dynamics of taxonomic bias in describing marine biodiversity, emphasizing the relationship between the size of species and the timing of their formal description. Analyzing data from over 62 000 marine animal species across nine phyla, we find a significant inverse correlation: smaller species are consistently described more recently. This pattern persists across taxa and habitats, with fluctuations observed over time, including notable inflections coinciding with historical events and technological advancements. These results underscore the implications of taxonomic bias for ecological understanding and conservation efforts, emphasizing the importance of completing inventories of marine life, particularly focusing on small‐bodied, potentially threatened species.

Multi-level feature fusion network for neuronal morphology classification

Neuronal morphology can be represented using various feature representations, such as hand-crafted morphometrics and deep features. These features are complementary to each other, contributing to improving performance. However, existing classification methods only utilize a single feature representation or simply concatenate different features without fully considering their complementarity. Therefore, their performance is limited and can be further improved. In this paper, we propose a multi-level feature fusion network that fully utilizes diverse feature representations and their complementarity to effectively describe neuronal morphology and improve performance. Specifically, we devise a Multi-Level Fusion Module (MLFM) and incorporate it into each feature extraction block. It can facilitate the interaction between different features and achieve effective feature fusion at multiple levels. The MLFM comprises a channel attention-based Feature Enhancement Module (FEM) and a cross-attention-based Feature Interaction Module (FIM). The FEM is used to enhance robust morphological feature presentations, while the FIM mines and propagates complementary information across different feature presentations. In this way, our feature fusion network ultimately yields a more distinctive neuronal morphology descriptor that can effectively characterize neurons than any singular morphological representation. Experimental results show that our method effectively depicts neuronal morphology and correctly classifies 10-type neurons on the NeuronMorpho-10 dataset with an accuracy of 95.18%, outperforming other approaches. Moreover, our method performs well on the NeuronMorpho-12 and NeuronMorpho-17 datasets and possesses good generalization.

An Insight into the Lived Experiences of Medical College Women Post-pandemic

The purpose of this study was to document and analyze the narratives of college women pursuing medical-related courses in the aftermath of the COVID-19 pandemic, emphasizing the multifaceted challenges they encountered and the strategies they employed to navigate these difficulties. The research aimed to provide a comprehensive understanding of how the pandemic has reshaped the educational landscape for these students, impacting not only their academic pursuits but also their mental health, social interactions, and overall well-being. Using a qualitative approach, the study employed in-depth semi-structured interviews with participants selected through purposive sampling, ensuring a diverse representation of experiences. The data were analyzed using thematic analysis, allowing for the identification of key themes related to the academic, emotional, and social dimensions of their experiences. By capturing their narratives, this study sought to enrich the existing body of knowledge regarding the pandemic's effects on higher education, particularly within the realm of medical education, which demands a unique set of skills and resilience. The significance of this research lies in its potential to inform educational institutions, policymakers, and support services about the specific needs and experiences of college women in medical programs. It highlights the importance of acknowledging gender-specific challenges and the necessity for tailored support systems. Ultimately, the findings aim to guide the development of gender-sensitive policies and interventions that foster a supportive educational environment, enhancing the holistic well-being and academic success of these students. This research not only addresses immediate concerns but also contributes to a broader discourse on equity and inclusion in higher education, advocating for a more responsive and adaptive approach to the evolving needs of college women in medical fields.

Teachers and Students’ Awareness of Re-Entry of Mother-Students Policy in Public Secondary Schools in Dodoma City, Tanzania

This study investigates the awareness levels among teachers and students regarding the re-entry policy for mother-students in public secondary schools in Dodoma City, Tanzania. The study employed a descriptive research design. A purposive sampling method was employed to select 10 of these 35 schools, ensuring a diverse representation from different areas within the city. The selected schools' population included 11,329 students and 512 teachers, from whom a sample of 489 students and 104 teachers was drawn. Data was collected using a self-administered questionnaire and analyzed through descriptive statistics and the Chi-square test. Based on the findings, the study concludes that gender and students’ education level determine the awareness of re-entry policies. Those students in higher classes had greater exposure to the policies than those in lower classes. Therefore, educational mobility expands individuals' access to policy discussion, calling for specific interventions among females and lower classes. The study suggests that professional development programs must increase teachers’ understanding of the re-entry policies, especially for female teachers. The school curriculums should introduce an extensive advocacy program for lower-level classes and girls. A fair outreach must be undertaken so that students, regardless of their socio-economic background, can know about reentry policies.

Omissions and Marginalization: Asian American Representation in Art History and Education

Asian Americans have been historically marginalized and underrepresented in U.S. history and education due to stereotypes and the essentialization of diverse ethnicities and cultures. This article explores the exclusion of Asian American arts and artists through the lens of Asian Critical theory. The near invisibility and misunderstanding of Asian American arts have impacted art education, which remains centered on Whiteness and Euro-Western perspectives. To disrupt the status quo, more artists and art educators are striving to enhance the representation of Asian American arts and address the racism against Asian and Asian American communities. The article suggests two pedagogical approaches: (a) designing an inclusive curriculum incorporating diverse representations of Asian American artists; and (b) creating spaces for students to engage in counterstorytelling. It concludes by urging art educators to incorporate Asian American arts and experiences and foster critical discussions on racism against Asian Americans in the U.S. in their teaching and research.

How is autism portrayed in news media? A corpus-based, mixed-methods analysis in China

IntroductionNews media play an important role in shaping public perceptions of and attitudes toward people with autism. This study examines news reports about autism in China Daily over the past 20 years, from 2003 to 2022.MethodsThis study employed both quantitative and qualitative analyses.ResultsThe findings reveal that news coverage on autism predominantly centers on children, with a significant shift in focus from support by family members and schools to support at the broader societal and governmental levels. Further, the overall representation of autism remains predominantly negative and rooted in prejudice. In terms of gender representation, autism is often depicted more frequently as male-oriented. However, a more diverse representation of age groups has emerged. Finally, while news media subtly convey negative attitudes toward autism, they also demonstrate a concurrent concern for the condition, reflecting a complex and nuanced attitude toward people with autism.DiscussionOur study indicates that corpus-assisted critical discourse analysis allows researchers to compare news over time, thus offering insights into changing discourses. The analysis can also reduce bias and uncover the implicit attitudes of news media toward autism by analyzing patterns of words and discourses.

The Creation Of The Composition Of The Musical Work Of Kemepyar As Representation Of Traditional Music Diversity

The creation of the work "kemepyar" is a response to the typical archipelago food containing many spices, one of which is soto. Soto is made from a variety of spices and spices from all corners of the country, be it in the form of tubers, leaves, grains, and so on. The mixture of various Indonesian spices produces a distinctive taste and makes the body feel warm and fit. This was used as an inspiration for the creation of the artwork entitled Kemepyar. Kemepyar is a term used by Javanese people to describe a situation that very bright, clear, warm, satisfying, satisfied, and delicious in enjoying a dish of food or drink with a fresh taste. The musical idea of the work comes from Javanese gamelan and Macapat dhandang gula. Musical idioms use Javanese, Balinese, Kalimantan, and Sumatran karawitan. Meanwhile, the medium of cultivation uses gamelan, swipe instruments, saxophone blowing instruments, and tambourines. The purpose of creating this work as a form of response to the social environment, in this case the diversity of spices/spices used in soto dishes as a representation of ethnic diversity and traditional music in Indonesia. This work is useful as a stimulus for Music Composition students to create works derived from the phenomenon of daily life, in this case food. The method of creation was chosen to realize the phenomenon of food diversity of soto spices into the form of musical works. The creation process consists of initial stimulation, idea emergence, exploration, improvisation, formation, and presentation.

Personality traits as predictors of forgiveness and gratitude/awe: a two-wave longitudinal study

AbstractCross-sectional studies have shown that individual differences can be associated with pro-social emotions. However, little is known about how personality may predict subsequent forgiveness and gratitude/awe. This study investigates the longitudinal influence of the Big Five traits (assessed by the Ten-Item Personality Inventory), supplemented by narcissism (assessed by the Single Item Narcissism Scale), on decisional and emotional forgiveness (assessed by the Decisional and Emotional Forgiveness Scales), as well as gratitude/awe (assessed by the Gratitude/Awe Questionnaire). Data were gathered from 292 respondents (64% women) from Poland who completed questionnaires at Time 1 (T1) and 6 months later at Time 2 (T2). Structural equation modelling showed that agreeableness at T1 positively predicted decisional forgiveness at T2; extraversion at T1 and emotional stability at T1 positively predicted emotional forgiveness at T2; and openness at T1 positively predicted gratitude/awe at T2, albeit all effects were weak. Our findings suggested that personality traits play a more minor role than one might think based on cross-sectional studies in determining individuals’ capacity for forgiveness and gratitude/awe. However, it is essential to note that these results are specific to the Polish population, underscoring the necessity for future research incorporating a more diverse demographic representation.

Marginalized measures: The harmonization of diversity in precision medicine research.

The production of large, shareable datasets is increasingly prioritized for a wide range of research purposes. In biomedicine, especially in the United States, calls to enhance representation of historically underrepresented populations in databases that integrate genomic, health history, demographic and lifestyle data have also increased in order to support the goals of precision medicine. Understanding the assumptions and values that shape the design of such datasets and the practices through which they are constructed are a pressing area of social inquiry. We examine how diversity is conceptualized in U.S. precision medicine research initiatives, specifically attending to how measures of diversity, including race, ethnicity, and medically underserved status, are constructed and harmonized to build commensurate datasets. In three case studies, we show how symbolic embrace of both diversity and harmonization efforts can compromise the utility of diversity data. Although big data and diverse population representation are heralded as the keys to unlocking the promises of precision medicine research, these cases reveal core tensions between what kinds of data are seen as central to 'the science' and which are marginalized.

Developing of a multi-angle and varied walking conditions dataset for human gait recognition

The field of Human Gait Recognition (HGR) leverages unique walking patterns for non-invasive, discreet biometric identification. This study highlights the importance of comprehensive datasets in the development, testing, and validation of HGR algorithms. While current datasets, such as those from Carnegie Mellon University Motion of Body (CMU MoBo), Southampton (SOTON), Chinese Academic of Science Institute of Automation (CASIA B), and Osaka University Institute of Scientific Industrial Research (OU-ISIR), have advanced in scale and complexity, they often lack diversity and comprehensive sample representation. To address this gap, we introduce the TecNM Gait-DS dataset (Tecnologico Nacional de Mexico), specifically designed for Latin American populations, featuring 13 viewing angles and five walking variations. Utilizing a Self-Supervised Vision Transformer DINO (Deeper Into Neural Networks) model for view angle classification, our evaluation demonstrates significant improvements in classification accuracy. This dataset not only enhances sample diversity but also supports the development of more robust HGR systems. Our results underscore the potential for improved accuracy and ethical considerations in HGR, advocating for ongoing refinement of datasets to achieve optimal performance and societal acceptance.

Leveraging Contrastive Language–Image Pre-Training and Bidirectional Cross-attention for Multimodal Keyword Spotting

In resource-limited keyword spotting scenarios, the scarcity of annotated corpora hinders deep learning’s ability to develop robust models for representing acoustic features. Recent studies focus on contrastive learning, using paired examples for self-supervision, indicating a growing research interest in keyword spotting. Typically, models are trained on segmented spoken words. However, the absence of distinct word boundaries in lengthy audio poses significant challenges. This study presents a novel approach that integrates Contrastive Language–Image Pre-Training with Cross-Attention for Self-Supervised Alignment in the field of Multimodal Keyword Spotting. The proposed method introduces a cross-modal process for matching word pairs, enhancing collaborative effectiveness between audio and text embeddings, achieving word-level congruence across different modalities. Employing a self-supervised learning approach, we use a restricted number of annotated audio–text pairs to discern semantic congruities and divergences, resulting in improved multimodal feature representation. Using this strategy, we input into subsequent keyword detection tasks, adopting a Bidirectional Cross-Attention block for detailed coordination across modalities, bridging the semantic gap between diverse audio and text representations, consequently improving keyword localization precision. Comprehensive experiments conducted on the Aishell-2 and Librispeech datasets demonstrate that the proposed method markedly surpasses existing techniques, especially regarding the comprehensive performance metric F1 for keyword spotting. It attains state-of-the-art outcomes for both pristine and noisy speech, showing respective enhancements of 5.2% and 5.4% on Aishell-2, as well as 5.4% and 5.6% on Librispeech.

Moisture content determination along production line of ibuprofen soft gelatin capsule manufacturing by near infrared spectroscopy and ensemble deep neural networks

AbstractIn the manufacturing process of ibuprofen soft gelatin capsules, controlling moisture content at each production line stage, including in their components—gelatin, fill content, and shell—is vital to ensure quality and stability. This study developed and assessed an analytical method for rapid and non‐destructive moisture determination using near‐infrared spectroscopy (NIRS) coupled with deep neural networks (DNN) on all stages of the ibuprofen production line. The NIRS‐DNN classifier models were able to distinguish between the three components, achieving accuracy scores of up to 99%. The DNN models for moisture quantification also demonstrated high accuracy, with R2 values exceeding 0.997 across all production stages and prediction errors to those of previously reported models. A significant advantage of the NIRS‐DNN approach was its ability to maintain accuracy over a wide moisture concentration range, from 7% to 48%. The ensemble model, NIRS‐EDNN, seamlessly integrated classification and quantification, revealing its potential for real‐time process control in soft gel manufacturing. The comprehensive sampling approach ensured a diverse representation of moisture content, thereby enhancing the understanding of its impact on the final product stability, demonstrating that this methodology is potentially applicable to any soft gelatin capsule tracking worldwide.

Public-engagement strategies of the South Asian COVID-19 Task Force: The role of racialized healthcare workers in COVID-19 mitigation in Ontario.

The COVID-19 pandemic began in late 2019 and its uneven impact across different communities globally was quickly evident. In Canada, South Asian communities were disproportionately affected. In response, the South Asian COVID-19 Task Force (SACTF) emerged, seeking to address the unique challenges faced by the South Asian community. The embedded single case study design was employed to explore the role of SACTF in COVID-19 mitigation in Ontario. Informed by critical race theory and a public engagement conceptual framework published by the Canadian Health Services Research Foundation (2010), we analyzed how contexts guided the goals, processes, and outcomes of SACTF activities. We conducted one-on-one semi-structured interviews and focus group discussions with SACTF's Board of Directors and analyzed SACTF-produced knowledge dissemination materials and media coverage of SACTF spanning March 2020 to February 2022. SACTF's success in educating and advocating for South Asians offers important insights into the gaps in public health communication and the inequities in healthcare delivery. It emphasizes the importance of tailoring emergency responses to community-specific needs and the role of racialized healthcare workers in facilitating trust-building within minority communities. By incorporating insights of racialized healthcare workers in health system decision-making, both public engagement and community health outcomes can be improved. This study contributes to a nuanced understanding of community-centric pandemic responses and demonstrates the need for diverse representation in decision-making processes for long-term health system resilience. Both healthcare knowledge and lived experiences made SACTF alert to how pandemics unfold differently and have differential effects on racialized populations. SACTF's responses offer practical recommendations for future pandemic preparedness and emergency responses, emphasizing the role of advocacy groups in addressing public health gaps and serving as crucial allies for communities and governments.

Modern pollen–plant diversity relationships for reliable pollen-based reconstruction of past plant taxonomic and functional diversity: A case study in southwest Shandong, China

Pollen diversity provides insights into our understanding of past plant diversity, a wide variety of diversity indices have been used to characterize plant diversity from sedimentary pollen. However, studies reported inconsistent associations between pollen-plant taxonomic diversity, this could be partly accounted for by biases due to interspecies differences in pollen production, pollen diffusion/deposition, and poorly specified dispersal distance of pollen-source plants. It is therefore crucial to improve the interpretation of the sedimentary pollen diversity by systematic studies of palynological and floristic diversity associations in contemporary landscapes.To test the reliability of pollen assemblages as a proxy of plant diversity, we collected pollen and vegetation from 36 surface sites in southwest Shandong. We detected the correlation between pollen-plant taxonomic and functional diversity indices from different spatial scales.Our results show pollen-plant correlations vary in spatial scales for different indices, the Spearman correlation analysis indicates significant and the strongest correlations between pollen-plant Shannon index around the RSAP (0.399 < ρ < 0.520), while the Pielou’s evenness shows significant and the highest correlations (0.411 < ρ < 0.425) around and beyond the RSAP, but only weak positive relationships can be detected between pollen-plant richness. Functional composition based on whole plant height and specific leave area and functional dispersion based on combined traits of whole plant height, specific leave area, seed mass, and leave dry mass shows significant positive pollen-plant associations, indicating that functional diversity may improve the pollen-plant link.Our results confirm that pollen data performs well in presenting plant taxonomic and functional diversity, which provides a solid theoretical basis for quantitative reconstruction of the long-term plant diversity dynamics and response of ecosystem functions to the environment in the future. Furthermore, the results demonstrate that the spatial scale of vegetation survey plays an important role in the representation of pollen diversity.

Shadow Representation: Making Claims to Represent Better Than the Official Representative

ABSTRACT This article advances knowledge on the diverse ways contemporary political representation is being performed. It identifies a particular kind of claim-making, shadow representation, whereby an individual or group makes claims to represent better than the officially authorised spokesperson or group. Shadow representation is enacted outside or alongside authorised spaces of representation by diverse actors such as civil society groups, lobbyists, political challengers, and former incumbents. The article conceptualises shadow representation in broad terms, and then examines its enactment in the electoral realm. The empirical analysis centres on two Australian cases in which defeated electoral candidates continue to make representative claims post-election. In both instances the shadow representatives construct a neglected constituency, and make claims to rectify representative deficiencies in the elected official. Their claim-making occurs between elections, and draws legitimacy from their strong community and voter support – two features that set them apart from the traditional political challenger focused on ousting an incumbent in an election. The article reflects on the democratic implications of shadow representation in electoral democracy: it potentially boosts the public scrutiny and accountability of elected officials, but it can also be used to destabilise the legitimacy of electoral procedures and their outcomes.