Five lessons from art for better research
Notice bibliographique
Résumé
‘I've never made a painting as a work of art, it's all research’ (Picasso) What can art teach us, not just for life: but also for research? The Arts are used to understand and represent human experience (Leavy 2013) and in health research and practice (Archibald et al. 2014). But false dichotomies abound: of art vs. science, aesthetics vs. method and expression vs. discipline. We highlight five lessons from art that can bring valuable benefits to research for meaning, creativity and motivation. ‘Why do you get out of bed in the morning? And WHY should anyone care’ (Sinek 2009, p. 39). Artists do not just do tasks but draw on and shape their identity deliberately through and in their work. This addresses what Sinek (2009) terms ‘the big WHY’ – the deeper values guiding work that relate to personal purpose and beliefs. This ‘why’ is different to the ‘how’, the ‘who’ and the ‘what’ of work. While the focus of research is likely to change over a career, values should transcend. Matisse's aim was to discover the essential character of things and to produce an art of balance, purity and serenity. By concentrating on the human figure, he was able, passionately, to express and explore his deepest feelings about life. This underlying focus persisted regardless of the various stylistic approaches he used over time (Neret 1996). Similarly, research should be about deeper values. The expression of which provides powerful transcending motivation (Hautula 2015). Knowing your values in this way counters increasingly common ‘questionable research practices’ (John et al. 2012) – the tendency to cut methodological corners to make research appear more affirmative and of more impact. Reflecting values through research is a vital aspect of integrity and can support researchers to avoid such practices by putting ‘courage over comfort … what is right over what is fun, fast or easy and … to practice our values rather than simply professing them’ (Brown 2015, p. 21). Accordingly, rather than detracting from the rigor of scientific inquiry, values are a vital facilitator of research ethics, credibility and rigor. Authenticity comes when personal values guide practice. It is through authenticity that artists and researchers can live and realize their vision and use creative practices to more effectively understand and communicate their place in the world. ‘The uniqueness of the art of a great painter is he is never satisfied with discoveries… and immediately launches into new directions’ (Neret 1996 p.10). Nineteenth century art critic Louis Leroy wrote a searing and very personal condemnation of Impression, Sunrise – an early Monet work (Gompertz 2016). Inadvertently, in his sarcastic criticism of Monet, Leroy coined the term ‘impressionism’ unintentionally leaving his own imprint on art history (Gompertz 2016). Yet, while Leroy publically mocked the furious painter, Monet continued unabated in his quest to paint light, not things – eventually becoming one of the most innovative and famous painters in history. Success and failure are unpredictable. Yet, how researchers handle these in their work is controllable (Clark & Sousa 2015). Monet, like virtually all artists linked to new movements, faced initial rejection, criticism or indifference. The innate dilemma of creation is to risk rejection by ‘exploring new worlds’ or court expedient acceptance via conventional paths (Bayles & Orland 1993, p. 43). Facing failure openly but not letting it damage or dent you is similarly vital for and in research (Clark & Sousa 2015). A guaranteed way of avoiding research failure is avoiding aspiration, difficulty and risk: publishing only in journals that you know are very likely to accept your paper, perpetuating your own ‘success narrative’ by acting entitled and superior with students or junior colleagues and never sharing your own failures. Getting it wrong is an inevitable companion of doing the right research. Researchers, like artists (Gompertz 2016), should keep extending their work and never truly ‘arrive’. ‘Everything connects’ (Da Vinci) Art offers different ways of seeing and understanding the world (Gompertz 2016). Similarly, research addresses existing problems in new ways and new problems with existing ways. This not only requires method, but also the scholarship of application. Artists are often wary of excessive narrowness and often consciously draw from a disparate range of influences to bring rich and fresh convergence to their work. Reflecting the ‘Scholarship of Integration’ (Boyer 1997), this process of drawing on wide influences recognizes that the process of creation involves multiple forms of interaction: human-human (meeting in person to discuss ideas), human-object (such as painting, sculpting, writing…. working and creating, regardless of medium) and human-object-human (learning from someone else's scholarship or art work). Sometimes, different bodies of knowledge in research can be seen wrongly as a distraction. The act of creation is reduced to long periods sitting in front of a computer screen trying to hack out the next words. Creativity requires reconciling the tension between creating work that is both ahead of its time and relevant to its current context. This requires openness and awareness of what has come before. Had Picasso lacked awareness of Matisse's Blue Nude (1907), he may never have created Les Demoiselles d'Avignon, one of the most famous examples of cubist art in existence. Similarly, researchers face the task of producing work that is not entirely derivative but that builds and draws from previous work, often linking these concepts together in new ways through a process of creative discovery. As you become more focused in your research efforts, reading and drawing on wide influences becomes even more important. ‘All things will be produced in superior quantity and quality and with greater ease, when ‘people’ work … in accordance with their natural gifts and at the right moment, without meddling’. (Plato) Artists and researchers face the extremely personal and ongoing challenge of reconciling the quality and quantity of their work with their values and aspirations. They face a tension between producing more or making better. While intimately related, notions of quality and quantity can vary in art and research – and much depends on personal vision and values. Work that is of insufficient quality is unlikely to be particularly influential in improving reputation even in high quantities. Like the artists of old who consistently sought and fought for their work to be recognized by the mainstream, it is important for those doing innovative research to seek for it to be influential and be featured in mainstream journals. As such, while quantity of work is no guarantee of quality, it is a prerequisite and the imperfections of your current work provide the seeds for your next inquiry (Bayles & Orland 1993). Ironically, for artists, more creative work often comes with the discipline of regular work patterns. As such, an exclusive preoccupation with quality can lead to perfectionism and disruptions to flow, which can debilitate even the most well-intended and talented person. Consequently, regularly engaging in research and writing and learning from missteps in the pursuit of precision – not perfection – provides an important basis to reconcile quality and quantity. ‘I never want to fully know what I am going to have at the end because then I wouldn't need to make the painting.’ (Enright 2014, interview with McIntosh, para. 2) Uncertainty characterizes the process of creation. A painter who knows precisely the outcome of their work embodies the spirit of craft, not art. A researcher who knows the result of a study prior to its commencement need not conduct it. Forcing hegemonic control over research and artistic processes risks the opportunity for creative discovery (Finley 2003). Accompanying uncertainty are fear and audacity – two embodied experiences that are inextricably linked to creativity (Clark & Thompson 2015). Fear has many potential roots: seeking approval within an esteemed circle, attaining the next grant or publication acceptance and uncertain futures. While fear can ignite motivation against stagnation, it can also stunt creativity. Yet audacity, if left unchecked, can also thwart striving and progress (Clark & Thompson 2015). Academics, like artists, are tasked with reconciling and thriving, within such tensions. For both artists and academics, merely recognizing uncertainty without embracing it is insufficient. Like artists who face rejection when innovating, researchers must handle a paradoxical bias against novelty in science: highly unique and influential articles often receive delayed recognition precisely because they are ahead of their time (Wang et al. 2016). Consequently, innovative research can be more difficult to publish and conflict with conventions and established interests. Such was the circumstance with Patricia Benner's much acclaimed work From Novice to Expert (Benner 1984); although commissioned by the American Hospital Association, they decided not to publish the results (Benner, personal communication). However, the work went on to transform thinking on how nurses learn to nurse. Leading successful research, in many ways, has never been as challenging. Ironically, this means that the lessons art offers for research have never been as potentially useful.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».