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The intention of this chapter is to show how autoethnographic research might promote reflexivity among career professionals. We aim to answer the question: can writing one’s own life and career story assist career practitioners and researchers in identifying patterns, idiosyncrasies, vulnerabilities that will make them more aware of the elements that are fundamental to career construction and that have been mentioned in a variety of disparate places in the existing career literature? What interested us as career researchers and co-creators of the narrative approach Career Writing in considering the innovative intention of this book, was how writing our own career story could deepen our professional reflexivity and might also help others to do so. https://doi.org/10.1007/978-3-030-22799-9_30 LinkedIn: https://www.linkedin.com/in/reinekke-lengelle-phd-767a4322/
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This paper presents a method for generating player-driven narratives from visual inputs by exploring the visual analysis capabilities of multimodal large language models. By employing Bartle’s taxonomy of player types—Achievers, Explorers, Socializers, and Killers—our method creates stories that are tailored to different player characteristics. We conducted a fourfold experiment using a set of images extracted from a well-known game, generating distinct narratives for each player type that are aligned with the visual elements of the input images and specific player motivations. By adjusting narrative elements to emphasize achievement for Achievers, exploration for Explorers, social connections for Socializers, and competition for Killers, our system produced stories that adhere to established narratology principles while resonating with the characteristics of each player type. This approach can serve as a helping tool for game designers, offering new insights into how players might engage with game worlds through personalized image-driven narratives.
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This article presents a novel and highly interactive process to generate natural language narratives based on our ongoing work on semiotic relations, providing four criteria for composing new narratives from existing stories. The wide applicability of this semiotic reconstruction process is suggested by a reputed literary scholar's deconstructive claim that new narratives can often be shown to be a tissue of previous narratives. Along, respectively, three semiotic axes – syntagmatic, paradigmatic, and meronymic – existing stories can yield new stories by the combination, imitation, or expansion of an iconic scene; lastly, a new story may emerge through reversal via an antithetic consideration, i.e., through the adoption of opposite values. Targeting casual users, we present a fully operational prototype with a simple and user-friendly interface that incorporates an AI agent, namely ChatGPT. The prototype, in a coauthor capacity, generates context-compatible sequences of events in storyboard format using backward-chaining abductive reasoning (employing Stable Diffusion to draw scene illustrations), conforming as much as possible to the user's authorial instructions. The extensive repertoire of book and movie summaries available to the AI agent obviates the need to manually supply laborious and error-prone context specifications. A user study was conducted to evaluate user experience and satisfaction with the generated narratives. The preliminary findings suggest that our approach has the potential to enhance story quality while offering a positive user experience.
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This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
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Background: The number of people with multiple chronic conditions requiring primary care services increases. Professionals from different disciplines collaborate and coordinate care to deal with the complex health care needs. There is lack of information on current practices regarding interprofessional team (IPT) meetings. Objectives: This study aimed to improve our understanding of the process of interprofessional collaboration in primary care team meetings in the Netherlands by observing the current practice and exploring personal opinions. Methods. Qualitative study involving observations of team meetings and interviews with participants. Eight different IPT meetings (n = 8) in different primary care practices were observed by means of video recordings. Experiences were explored by conducting individual semi-structured interviews (n = 60) with participants (i.e. health care professionals from different disciplines) of the observed team meetings. The data were analysed by means of content analysis. Results: Most participants expressed favourable opinions about their team meetings. However, observations showed that team meetings were more or less hectic, and lacked a clear structure and team coordinator or leader. There appears to be a discrepancy between findings from observations and interviews. From the interviews, four main themes were extracted: (1) Team structure and composition, (2) Patient-centredness, (3) Interaction and (4) Attitude and motivation. Conclusion: IPT meetings could benefit from improvements in structure, patient-centredness and leadership by the chairpersons. Given the discrepancy between observations and interviews, it would appear useful to improve team members’ awareness of aspects that could be improved before training them in dealing with specific challenges.
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Writing as soul work refers to the active engagement of students in transformative writing activities in a group setting with the aim to enable students to develop new, more empowering narratives. This article explains how soul work through writing can be used to foster career adaptability, expressed in the form of increased awareness and self-direction. We summarize the labour market realities that underlie a need for more narrative approaches and introduce writing as soul work as a potential method to respond to these contemporary career challenges. We define what is meant by soul work and writing, illustrate its use with several stories from practice, and make recommendations for teachers and implementation in institutions. “This is an Accepted Manuscript of an article published by Taylor & Francis in "British Journal of Guidance and Counsellingon" on 04/16/2016 available online: https://doi.org/10.1080/03069885.2016.1169366 LinkedIn: https://www.linkedin.com/in/reinekke-lengelle-phd-767a4322/
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IntroductionOver time, surrogacy has become more broadly available to a variety of people (e.g. male same-sex couples or transgender women). Whether the wider public supports surrogacy, and what contributes to such support remains unclear. This study investigated what demographic and surrogacy arrangement-based (which people participate in the arrangement) factors shape attitudes towards surrogacy.MethodA representative sample of Dutch adults (N = 1,074) reported their attitudes on four (out of 30) randomly assigned vignettes in 2023. Each vignette described a surrogacy family with variations in sexuality and gender of parents, the social and genetic bonds between the parents, the surrogate, and the oocyte donor, and was followed by an attitude questionnaire (6 items). Multilevel regression analyses were conducted with attitudes as the dependent variable and demographic factors (gender, Dutch background, age, education, sexual orientation, urbanisation, and religiosity) and arrangement-based factors (parental composition, genetic and social bonds with the surrogate, and oocyte donors).ResultsParticipants held fairly positive attitudes towards surrogacy. People identifying as women, with only having a Dutch background, who were younger, more highly educated, non-heterosexual, or less religious were more likely to have positive attitudes. Participants had more positive attitudes if surrogacy arrangements entailed cis-man cis-woman parents compared to cis-man cis-man or transgender parents, and when there was no social bond between parents and oocyte donor.ConclusionsAttitudes are influenced by both demographic and arrangement-based factors. Based on these findings, families can be informed of fairly positive reactions they might encounter from their environment.
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