Understanding Of Context Research Articles

Contemporary understanding of health literacy across the Island of Ireland

Abstract This presentation will detail two reviews that provided contextual understanding for this project. The first, an umbrella review which explored i) How is health literacy currently defined and operationalised nationally and internationally? And ii) What are the key components and principles of health literacy? The search was limited, most notably, to systematic reviews published after 2012. A total of 12 studies were included for data extraction and narrative analysis. Findings provided an update of the conceptual understanding of health literacy, emphasising the complex and multidimensional nature of the concept. Dimensions are common, but growing, and moving beyond just individual capabilities. The second, moving beyond the definition, details a systematic review that aimed to gain a contemporary understanding of health literacy through identifying and appraising existing research conducted in an Irish context. Seven electronic databases were searched between October and December 2023. Initial search identified 551 records, following screening, 37 studies were included in this review. Narrative analysis indicated that across the Island of Ireland in the last decade, many studies had narrow populations of focus (i.e. Dublin based, adults, chronic illness populations), limited research design and methodologies (cross-sectional, narrative, and primary research with short time frames), and lacked rigorous monitoring and evaluation of health literacy as a primary or secondary outcome. While studies highlighted clinical, education and community implications, a limited focus on mental health was also observed. Findings from this systematic review offer valuable insights and recommendations for future efforts to enhance health literacy and the health and well-being of individuals across the island of Ireland.

Evaluating the Translation Accuracy of ChatGPT and DeepL Through the Lens of Implied Subjects

This research aims to offer deeper insights into the translation quality of large language models and neural machine translation systems. It compares the performance of two state-of-the-art models, ChatGPT and DeepL, to explore which system achieves higher translation accuracy. This research employs a highly focused and specialized method to evaluate translation performance, enabling the identification of nuanced differences between advanced AI/machine models. It focuses on implied subjects in contemporary Chinese writer Qiuyu Yu’s Iraq travelogue and examines how the two models handle these implied subjects when translating the text into English. The analysis shows that both ChatGPT and DeepL exhibit high-level intelligence when dealing with the implied subjects. They can flexibly adjust sentence structures to maintain the implicitness or to make the implied subjects explicit. The analysis further reveals that ChatGPT performs better in accurately inferring the implied subjects, which thereby reflects ChatGPT’s superior contextual understanding capability. The comparison of the source text and target texts also shows that ChatGPT’s translation is more succinct, nuanced, and easier to read. DeepL’s translation, by comparison, prioritizes reducing potential misunderstanding. This research tentatively concludes that, though not specialized for translation, ChatGPT translates with higher accuracy and demonstrates more sophisticated intelligence compared to DeepL when translating literary texts.

A pedagogical evaluation of an institution's digital assessment platform (DAP): Integrating pedagogical, technical and contextual factors

An assessment for learning approach is foundational for student learning. The necessity to shift teaching and learning online as a response to COVID-19 has propelled digital assessment into the mainstream within higher education institutions. User experience is a common indicator of effectiveness of technologically enhanced initiatives; however, measuring the pedagogical impact of digital assessment initiatives has not been straightforward. This paper presents a pedagogical evaluation, incorporating staff and student perspectives, of a digital assessment platform (DAP) at a large Australian metropolitan university. Data were collected using surveys, interviews, focus groups and case studies and incorporated findings from student evaluations of courses and a student-staff partnership project. Results highlight strong pedagogical reasons for continuing the digital assessment initiative, while also identifying constraints and opportunities for improvement. The insights generated through this evaluation emphasise the importance of considering technical, pedagogical and contextual factors and the interplay between the factors. In a quality assurance climate, where the demand for this type of institution-wide evaluation is increasing, this study contributes an approach that addresses some of the complexities and challenges of evaluating digital assessment initiatives in higher education. Future research is needed to understand more fully the relationships between pedagogical and contextual factors when undertaking an evaluation. Implications for practice or policy: The DAP enabled the (re)design of authentic, inclusive, engaging and secure assessment tasks. Educator support is critical in learning the system and creating assessment with high pedagogical merit. Student familiarisation opportunities with using the DAP are beneficial. The DAP’s longevity within the institution is crucial considering the significant effort required of educators and students. Contextual influences blur understanding of pedagogical factors.

An ensemble deep learning model for medical image fusion with Siamese neural networks and VGG-19.

Multimodal medical image fusion methods, which combine complementary information from many multi-modality medical images, are among the most important and practical approaches in numerous clinical applications. Various conventional image fusion techniques have been developed for multimodality image fusion. Complex procedures for weight map computing, fixed fusion strategy and lack of contextual understanding remain difficult in conventional and machine learning approaches, usually resulting in artefacts that degrade the image quality. This work proposes an efficient hybrid learning model for medical image fusion using pre-trained and non-pre-trained networks i.e. VGG-19 and SNN with stacking ensemble method. The model leveraging the unique capabilities of each architecture, can effectively preserve the detailed information with high visual quality, for numerous combinations of image modalities in image fusion challenges, notably improved contrast, increased resolution, and lower artefacts. Additionally, this ensemble model can be more robust in the fusion of various combinations of source images that are publicly available from Havard-Medical-Image-Fusion Datasets, GitHub. and Kaggle. Our proposed model performance is superior in terms of visual quality and performance metrics to that of the existing fusion methods in literature like PCA+DTCWT, NSCT, DWT, DTCWT+NSCT, GADCT, CNN and VGG-19.

Issues in Machine Translation with reference to the Story “Thakur ka kuan” by Munshi Premchand

This paper explores the issues encountered in machine translation, specifically using Google Translate, in translating Munshi Premchand's short story Thakur Ka Kuan ("Thakur's Well"). The study identifies key challenges, including the misinterpretation of cultural references, idiomatic expressions, and contextual meaning that are integral to Premchand's narrative style. Machine translation often produces literal translations that lack the nuance and emotional depth present in the original text, leading to misrepresentation of the story’s themes. For instance, socio-cultural terms and metaphors central to the story's message are frequently mistranslated or omitted entirely. The translation also struggles with maintaining consistency in tense and sentence structure, further diluting the narrative. This paper argues that while machine translation offers convenience for basic translations, it remains insufficient for complex literary works, where cultural, emotional, and contextual understanding is crucial. A comparative analysis with a human-translated version highlights these deficiencies and suggests that human input remains essential for accurate literary translations. The findings underscore the need for improved machine learning models to address these linguistic and cultural intricacies.

Ecological empathy: relational theory and practice

ABSTRACT Sustainability science researchers are increasingly interested in human–nature connection as a leverage point for societal transformation. Empathy has potential as a way to reconnect people to nature by building relationships among more-than-humans. However, current approaches to empathy with more-than-humans usually prefer sympathy and compassion. I argue that these approaches limit the potential of empathy when considering human–nature (re)connection. I use the established concept of social empathy (Segal 2011, 2018) to structure a new presentation of empathy with more-than-humans: ecological empathy. Ecological empathy, as presented in this paper, consists of two subcomponents: contextual understanding of more-than-human interdependencies and more-than-human awareness and earth system perspective-taking. From this new perspective, I suggest defining ecological empathy as a cognitive and affective ability, which allows for internal coherence across bodily separation in humans and their environment. Integrating literature from biophilia, deep ecology, embodied cognition and multi-species ethnography, I elaborate on ecological empathy with inspirational practices that can be advanced across a range of decision, policy and design environments to address human–nature (re)connection.

At-risk or a risk? SENCOs’ conceptualisations of vulnerability and risk in relation to school exclusion

ABSTRACT In this paper we explore how Special Educational Needs Coordinators (SENCOs) conceptualise vulnerability and risk, and how these conceptualisations inform their responses to students at risk of exclusion from school. The literature typically makes a distinction between within-child and systemic or structural factors. We draw on interview data from SENCOs working in 11 secondary schools in England, and an activity theory approach to unpick the contradictions and tensions in their conceptualisations and responses. Overall, we identify a mismatch between SENCOs’ conceptualisations of risk and vulnerability intersecting at multiple levels, and interventions that are primarily located at an individual level. National policy, with its shift away from a contextual understanding of difficulties and difference towards a requirement for clinical diagnosis to access additional funding plays a key role in mediating or shaping SENCO responses, including the tools that schools use to identify at-risk students. The conceptualisations indicate a source of differential impact with some students shifting from being seen as ‘at-risk’ and requiring support, to being ‘a risk’ and better educated elsewhere. In the face of these conceptualisations, we argue for the place of a social relational understanding, emphasising the dialogic spaces in which behaviour is framed, and interventions planned.

A ‘Back Door’ Exploration of Social Problems in Comic Strip Dialogues With Marginalized Children

ABSTRACTBased on 35 comic strip dialogues with marginalized children in Italy, Norway and Slovenia, as well as three individual interviews with Italian practitioners, this article introduces a methodological approach that offers a ‘back door’ for exploring social problems embedded in the everyday lives of children. The analysis demonstrates the importance of adopting an everyday life perspective as an analytical strategy in conversations with children in marginalized life situations. This means shifting the focus away from solely examining risk factors and instead promoting a contextual understanding of risk, with an emphasis on amplifying the diverse voices of children.

Congruences of factor solutions of quantitative indicators for understanding the written form of deaf children's communication through the use of words types

In deaf students, there is no contextual understanding and recognition of word types through linguistic competence testing compared to the hearing population, because 67.10% do not understand, and 10.00% of deaf respondents partially understand the contextual application of word types in a written text task. In written communication, deaf students use nouns (65.70%), pronouns (34.30%), verbs (45.7%), adjectives (28.60%), adverbs (22.90%), prepositions (54.30%), exclamations (15.70%), particles (12.90%) and numbers (32.90%). The aim of the study was to examine the factor solutions of quantitative indicators of contextual understanding of the word’s types used and the relation of isolated factors of the words types words in the written text task of deaf students. The study was conducted on a sample of 140 respondents. The first subsample of respondents, the experimental group consisted of 70 deaf students, and the second subsample, the control group of 70 hearing students, of the same chronological age and gender. The measurement instrument “Test of comprehension of the written form of expression” was applied. The collected data were processed using the method of oblique rotation and the method of factor analysis. The results of the study showed that the language discourse of the deaf in the quantitative use of word types in the written form of expression is organized significantly differently compared to the hearing population. By factor analysis, three main factors were isolated in the deaf population, and four main factors were isolated in the hearing population. Considering the saturation of variability, it is possible to accurately determine in practice the direction of teaching deaf children the types of words in their native language. Isolated factors are part of separate, statistically unrelated entities in both deaf and hearing respondents and confirm the justification of successive teaching of the deaf respondents according to the types of words that are present in the comparative analysis of hearing respondents.

Joint intent detection and slot filling with syntactic and semantic features using multichannel CNN-BiLSTM

Understanding spoken language is crucial for conversational agents, with intent detection and slot filling being the primary tasks in natural language understanding (NLU). Enhancing the NLU tasks can lead to an accurate and efficient virtual assistant thereby reducing the need for human intervention and expanding their applicability in other domains. Traditionally, these tasks have been addressed individually, but recent studies have highlighted their interconnection, suggesting better results when solved together. Recent advances in natural language processing have shown that pretrained word embeddings can enhance text representation and improve the generalization capabilities of models. However, the challenge of poor generalization in joint learning models for intent detection and slot filling remains due to limited annotated datasets. Additionally, traditional models face difficulties in capturing both the semantic and syntactic nuances of language, which are vital for accurate intent detection and slot filling. This study proposes a hybridized text representation method using a multichannel convolutional neural network with three embedding channels: non-contextual embeddings for semantic information, part-of-speech (POS) tag embeddings for syntactic features, and contextual embeddings for deeper contextual understanding. Specifically, we utilized word2vec for non-contextual embeddings, one-hot vectors for POS tags, and bidirectional encoder representations from transformers (BERT) for contextual embeddings. These embeddings are processed through a convolutional layer and a shared bidirectional long short-term memory (BiLSTM) network, followed by two softmax functions for intent detection and slot filling. Experiments on the air travel information system (ATIS) and SNIPS datasets demonstrated that our model significantly outperformed the baseline models, achieving an intent accuracy of 97.90% and slot filling F1-score of 98.86% on the ATIS dataset, and an intent accuracy of 98.88% and slot filling F1-score of 97.07% on the SNIPS dataset. These results highlight the effectiveness of our proposed approach in advancing dialogue systems, and paving the way for more accurate and efficient natural language understanding in real-world applications.

Meningkatkan Sikap Kritis Siswa dengan Model PBL berbantuan Media Video Fase E Kelas X di SMA Negeri 4 Pematangsiantar

This research aims to enhance the critical thinking skills of 10th-grade students at SMA Negeri 4 Pematangsiantar through the implementation of the Problem-Based Learning (PBL) model, supported by video media. The students' critical thinking is assessed based on their ability to analyze information, distinguish between facts and opinions, and evaluate the reliability of the information they receive, particularly in relation to the influence of mass media. This study uses a Classroom Action Research (CAR) design consisting of two cycles, with each cycle including the stages of planning, implementation, observation, and reflection. The results of the research showed a significant improvement in students' critical thinking from the first cycle to the second. In the first cycle, the average critical thinking score was only 68%, with the main weakness being the evaluation of information reliability. However, after improvements in the second cycle, the average score increased to 90%. Students became more engaged in group discussions, better able to analyze videos, and more critical in evaluating media content. The use of video as a learning medium was also found to be effective in facilitating visual and contextual understanding of the material. The research employed a qualitative method with a descriptive approach. Data collection techniques included observation, interviews, and documentation. The data analysis techniques involved data reduction by selecting raw data into useful information, data presentation in the form of descriptions, graphs, and tables, and drawing conclusions based on the research findings that addressed the research questions. The use of the Problem-Based Learning model, supported by video media, followed the PBL syntax, including steps such as orienting students to the problem, organizing students for learning, guiding individual or group investigations, developing and presenting results, and analyzing and evaluating the problem-solving process. This approach proved effective in enhancing student motivation and independence in critical thinking and responsibility towards the influence of mass media in the 10th-grade class at SMA Negeri 4 Pematangsiantar. In conclusion, the implementation of the PBL model with video media significantly improved students' critical thinking skills. Recommendations include optimizing the use of video, providing more intensive support for students in need, strengthening group discussions, and continuing critical thinking exercises.

GS–CDNet: a remote sensing image cloud detection method with geographic spatial data integration

ABSTRACT In optical remote sensing images, clouds exhibit irregular scales and boundaries that vary with elevation across diverse geographical locations. To accurately capture the diverse visual patterns of clouds, we propose a cloud image segmentation approach named GS-CDNet (Geographic Spatial Data-Cloud Detection Network), which is based on the integration of geospatial data with multifaceted self-attention feature extraction, multi-scale feature aggregation, and boundary clarification techniques.Firstly, we utilize geographical coordinates from optical remote sensing images to extract a raster DEM (Digital Elevation Model) from SRTM3. This process creates a dataset consisting of elevation images, longitude, and latitude maps as geospatial data, enhancing the model’s capability in spatial positioning for cloud detection. Secondly, the proposed method consists of three interconnected modules within the cloud detection network: the Interleaved Self-Attention module(ISAM) utilizes a variety of self-attention mechanisms in an interleaved manner to extract multi-scale feature information.The Bidirectional Multi-Scale Feature Fusion Module(BIMFM) is responsible for integrating features, enabling a more comprehensive contextual understanding. The Boundary Extraction Module(BEM) utilizes a residual structure to generate a boundary cloud mask, effectively addressing the common issue of boundary blurring in multi-scale cloud masks. Finally, we compared and evaluated GS-CDNet with other cloud detection methods and conducted an ablation study on the key components of the method. The validation of generalization performance demonstrates the exceptional performance of the proposed model in cloud mask generation. Geospatial data and the different modules of the method play a significant role in the model.

Aging With Emerging Social Welfare and Disappearing Filial Piety: Chinese Migrant Workers' Pathways Toward Financial Retirement Planning.

This study examines the pathways and mechanisms linking Chinese migrant workers' migration experiences and their financial retirement planning. Using a mixed-methods approach with 1083 surveys and 32 interviews, this study finds that having a good financial status and social support system and maintaining a hopeful attitude toward retirement are direct pathways toward good financial retirement planning. Good health and hope for retirement are further enhanced by a good financial status and social support. Conversely, poor health and negative employment experiences are linked to poor financial retirement planning. The qualitative findings provide a contextual understanding of the pathways identified in the quantitative analyses. Migrant workers often face a dilemma between self-reliance for retirement and relying on filial piety. These findings apply not only to Chinese migrant workers but also to all migrant workers with limited access to healthcare and public pensions for retirement.

The Relevance of Indicator Assessment for Planning and Decision-Making in Urban Monitoring

Objective: The objective of this study is to investigate the application of information systems in urban monitoring management, specifically focusing on the analysis of indicators that can support decision-making. The study aims to explore which indicators are associated with monitoring management, their correlations with various provided services, and the triggers that activate these services. Theoretical Framework: This research is grounded in Contingency Theory, which posits that organizational effectiveness is contingent on how practices are adapted to specific contexts. Key concepts include the integration of information systems, data-driven decision-making, and urban management efficiency. Method: The methodology employed involves a combination of quantitative and qualitative data collection and analysis methods. This approach provides a comprehensive and contextualized understanding of urban monitoring operations. Data collection methods include [specify methods used, e.g., surveys, interviews, or observations]. Results and Discussion: The findings demonstrate the critical role of integrating information systems with everyday monitoring practices, allowing for the extraction and application of accurate and relevant data to enhance urban service management. The discussion highlights the implications of these findings within the theoretical framework, examining the relationships and discrepancies identified and considering the study's limitations. Research Implications: The study provides both theoretical and practical implications, offering insights into how the findings can be applied to improve practices in urban management. The implications are particularly relevant for enhancing the responsiveness and adaptability of urban monitoring strategies. Originality/Value: This study contributes to the literature by offering a novel perspective on the integration of information systems in urban monitoring, highlighting the importance of data-driven decision-making. The research's relevance is underscored by its potential to influence urban management practices and improve service quality.

Applying cultural perspective in Indonesia municipal solid waste management process towards a grounded climate concept and action: A mini-review of opportunities and challenges.

This mini-review emphasises the role of municipal solid waste (MSW) as the biggest contributor to climate change, as well as the need for more grounded climate action. The UN Framework Convention on Climate Change (UNFCCC) 2023 Synthesis Report by the co-facilitators on the technical dialogue Key Finding 3 of applying the 'whole-of-society' approach in this article is interpreted as a cultural approach in MSW management planning and implementation process. Using anthropological critiques of development, the cultural approach is frequently considered an obstacle or a justification for a project's failure rather than an important aspect of the people being developed. Therefore, the goal of this mini-review is to showcase the findings and explore the practical application of UNFCCC Key Finding 3, emphasising its importance in every phase of the solid waste management process for climate action. This mini-review argues that applying a cultural perspective presents both opportunities and challenges. More importantly, without careful acknowledgement and consideration, opportunities can become challenges, if not vice versa. The discussion section explores the ways in which religious and economic conditions might offer a contextual understanding and effective techniques for managing MSW at the local level. To apply Key Finding 3 in practice, academia needs to move away from generality and embrace multiple 'modernities', while practitioners also need to include cultural perspectives to complement scientific knowledge.

Recontextualising the Unified Theory of Acceptance and Use of Technology (UTAUT) Framework to Higher Education Online Marking

The Higher Education (HE) sector has faced an exceptional period of complex and fast-moving change, meaning staff have had to quickly adjust their teaching delivery, reconsider how learning outcomes are assessed, and adopt new technologies. Despite significant efforts by HE staff across these areas, the willingness to adopt new tools and technologies is not consistent across all staff cohorts and indeed some researchers suggest that this willingness, or readiness to adopt emerging technologies and educational tools can be shaped by individual differences, attitudes, and social norms.This study focuses on education technology acceptance in HE via a specific university and explores the use of technology enhanced feedback and marking tools, such as online embedded rubrics. Underpinned by the unified theory of acceptance and use of technology (UTAUT), this research project aims to explore the perceptions of staff towards online feedback and marking practices. In turn, the research project considers the relevancy of the UTAUT model in differing / current contexts. Contribution manifests through a) the methodological choice of qualitative semi-structured interviews to explore the UTAUT dimensions, b) the deep contextual understanding of the challenges HE staff face in adopting and implementing new technologies, c) suggested adaptations to UTAUT, and c) recommendations for HE practice.

Harnessing AI for Enhanced Searching in Digital Libraries: Transforming Research Practices

The application of Artificial Intelligence (AI) technologies in digital libraries is changing the paradigm in which users search and interact with digital content. This article is focused on how the use of AI technologies improves the search in digital libraries, demonstrating the benefits of such technologies in overcoming the challenges associated with formulation of query, information retrieval, and end user usage. Most search engine systems face the problems of user queries, which are inherent in online searches. Many user queries are vague, imprecise or ambiguous with regards to the context and as a result lead to less than satisfactory search results. AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) solve this challenge effectively as they enhance comprehension of queries, contextual understanding, and relevance of the results returned. With the capabilities of NLP incorporated in search engines, search queries which are in the form of natural language can be understood and executed which leads to efficiency in targeting the information needed. Comprehensive restructuring of standard information systems is used by computer learning algorithms for searches by inference and instills usage in users. This study also examines the place of AI in query formulation and the different ways it can be employed; fully automated, semi-automated and/or manually so that the users are less faced with the challenges that are usually present during the query formulation process. Such increases in the level of difficulty and scope of information retrieval suggest that search AI improved the scenario.

Utilizing ChatGPT for On-the-Fly Decision-Making in Autonomous Systems

In the realm of autonomous systems, rapid and efficient decision-making is critical to ensure optimal performance and adaptability in dynamic environments. This paper explores the application of ChatGPT, a large-scale language model, in on-the-fly decision-making within autonomous systems. By leveraging ChatGPT's capabilities in natural language processing, contextual understanding, and real-time response generation, the study demonstrates how it can support autonomous agents in interpreting situations, generating actionable insights, and making informed decisions. The integration of ChatGPT into autonomous systems provides a flexible and scalable approach to improving decision accuracy and response times in unpredictable scenarios. This research investigates the benefits, challenges, and potential use cases, highlighting the model's contributions to enhancing autonomy in various fields such as robotics, self-driving vehicles, and intelligent drones.

Trombone as a solo instrument in the light of organology and historiography

Problem statement. In contemporary instrumentology, the primary task is to identify not only technical but also semantic (artistic) qualities of instruments that belong to the general concept of “instrument style”. This regards trombone artistry, the study of which extends beyond instrumentation and intersects with issues in philosophy, aesthetics, and culture studies. Such contextual understanding of trombone style is presented in this article, which is dedicated to studying the least researched aspects of trombone creativity and performance — solo trombone. The purpose of this paper is to identify the organology characteristics of the instrument within the context of researching the solo trombone. The relevance of the article lies in the demand for comprehensive developments of instrumental styles, including trombone, in contemporary theoretical musicology, as well as the absence of works dedicated to incorporating the corresponding musical material required for developing a similar theme. The results. From the perspective of organology as a comprehensive science of instruments as “organs”, “tools” of musical thinking, trombone is a melodic instrument of the brass aerophones group, which, thanks to its slide, can reproduce segments of the chromatic scale within three octaves with the possibility of natural glissando. The trombone’s acoustic and semantic resources are combined in its stylistic image, allowing for multifunctional use of this instrument in various music genres. The article outlines the path of forming the solo qualities of the trombone, presents classification of the types of trombone soloing, among which solo unaccompanied works for the trombone are highlighted. These mostly include miniatures of both programmatic and non-programmatic content, as well as sonatas for the trombone presented in the works by both foreign and Ukrainian authors. The specificity of such works is concluded in the combination of concert characteristics (the principle of soloing) and chamber characteristics (reproduction in a single voice of ensemble functions with equally potent voices-registers). This layer of trombone creativity is the least researched and therefore requires separate study, the beginning of which is laid out in this article. The scientific novelty of the research. Solo trombone music becomes an autonomous center containing everything characteristic of the trombone image and, within its competence, demonstrates all genres that exist or have existed in it. The proper qualities of the solo trombone are revealed in unaccompanied miniatures, the characteristics of which are illuminated for the first time in this article. Therefore, the issue of involving self-sufficient solo trombone works into musicological discourse pertains not only to the technical aspect but also contributes to a new perspective on the aesthetics and poetics of this instrument, its new philosophy, as demonstrated by the music of the Modern era.

Enacting ‘true business sustainability’ − Market shaping for environmental impact

Market-shaping research on firms’ efforts to transform markets for environmental aims is scarce and limited to incumbent actors’ efforts to replace existing technologies with more sustainable alternatives. While important, these studies implicitly adhere to the ‘business case for sustainability’ approach, in which CSR is harnessed to facilitate profitability and competitive advantage. We offer a new perspective by examining market shaping explicitly aimed at inducing environmental impact. Using the lens of for-profit hybrid firms, which enact a ‘true business sustainability’ approach by blending environmental impact goals with commercial objectives, we explore the case of a hybrid shaping an existing EU regulation-born ‘sustainability market’ built around the Guarantees of Origin for renewable electricity. We contribute to the contextual understanding of market shaping for sustainability by presenting a model of firm-level market shaping for environmental impact, unfolding through six intertwined processes inherently connected to the organization’s development.