Verstehen for Sociology: Comment on Watts

Turco, Catherine J.; Zuckerman, Ezra W.

doi:10.1086/690762

Cited by 32 publications

(9 citation statements)

References 34 publications

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“…Second, HMC does not restrict itself to the commonly used statistical models offered by DMC for statistical and causal inference but incorporates the range of powerful algorithms offered by AMC (Bail, 2017;Lazer et al, 2020;Nelson, 2020;Turco & Zuckerman, 2017;Watts, 2017). As scholars combine AMC-type algorithms for DMC-inspired inference (Lundberg et al, 2021;Molina & Garip, 2019), they erode the traditional distinction between prediction and statistical inference .…”

Section: Discussionmentioning

confidence: 99%

“…While the distinction between predictive and causal statements has contributed to holding the truce among different quantitative research traditions (Freedman, 1991;Watts, 2014), unease is rising as scholars are increasingly using machine learning (ML) algorithms to analyze social phenomena (Bail, 2017;Lazer et al, 2020;Molina & Garip, 2019;Nelson, 2020;Shmueli, 2010;Turco & Zuckerman, 2017;Verhagen, 2022;Watts, 2017). ML is the study of how algorithms can learn from data (e.g., past social events) with no or little human guidance, thereby predicting new data instances (e.g., future social events) (Hastie et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Statistical Modeling: The Three Cultures

Daoud

Dubhashi

2023

Harvard Data Science Review

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Social scientists distinguish between predictive and causal research. While this distinction clarifies the aims of two research traditions, this clarity is blurred by the introduction of machine learning (ML) algorithms.Although ML excels in prediction, scholars are increasingly using ML not only for prediction but also for causation. While using ML for causation appears as a category mistake, this article shows that there is a third kind of research problem in which causal and predictive inference form an intricate synergy. This synergy arises from a specific type of statistical practice, guided by what we propose, the hybrid modeling culture (HMC). Navigating through a parallel debate in the statistical sciences, this article identifies key characteristics of HMC, thereby fueling the evolution of statistical cultures in the social sciences toward better practicesmeaning increasingly reliable, valid, and replicable causal inference. A corollary of HMC is that the distinction between prediction and causation, taken to its limit, melts away.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistical Modeling: The Three Cultures

Daoud

Dubhashi

2023

Harvard Data Science Review

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“…We will draw on the sociological concept of Verstehen, first advanced by Dilthey and then Weber (Brewer, 2011;Sumner, 2011), to suggest a more nuanced approach. Verstehen sociology emphasises the necessity of understanding the meaning of human action (Sumner, 2011;Turco and Zuckerman, 2017). It is not, however, about researchers plunging headfirst into the experiences or others, nor is it about researchers revealing their own personal information to research participants or to the wider public.…”

Section: Managing Emotions 'Productively' In Researchmentioning

confidence: 99%

Almost Confessional: Managing Emotions When Research Breaks Your Heart

Reed

Towers

2021

Sociological Research Online

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Social scientists have increasingly shown how qualitative research can be an emotional experience for researchers. Literature on this subject has tended to focus on the emotionally upsetting impact of data collection, often framing this as a form of emotional labour which can be managed by researchers adopting confessional style narratives throughout the research process. But what about the potentially life-affirming impacts of emotions in research? And what happens when confessional style narratives create, rather than dilute, emotional trauma? We use our experiences of conducting qualitative research on two very emotive topics – baby loss and sibling bereavement – to explore the role of emotions in research. We go beyond the predominant focus on doing research to shed light on emotions in the wider research process (from recruitment to impact). We will highlight the dual-edged nature of emotions in research, emphasising some of the more beneficial impacts. Drawing on the Weberian concept of Verstehen which focuses on the importance of understanding, we will also develop a more nuanced form of emotion management in this context. In doing so, we offer an original contribution to methodological discussions in this field, as well as to more conceptual debates on emotional labour.

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“…Fondant cette proposition sur une critique incisive des explications interprétatives qui sont au coeur de la tradition sociologique, le texte n'a pas été sans susciter de réponses. Catherine Turco et Ezra Zuckerman, deux sociologues du MIT (Turco et Zuckerman, 2017), ont par exemple reproché à Watts d'attribuer à la méthode interprétative en sociologie des problèmes qui relèvent en fait de la nature réflexive des sociétés humaines et des limites qu'elle pose à la prédiction en sciences sociales. Illustrant le type de propositions qui alimente les craintes d'une « colonisation » des sciences sociales par les sciences informatiques (McFarland et al, 2016), l'article de Watts invite les sociologues à s'engager dans une réflexion sur les chemins à prendre pour imaginer la sociologie numérique, face à l'avancée inéluctable des épistémologies propres aux physiciens et aux informaticiens.…”

Section: L'épistémologie Des Traces Numériques : Héritages Tensionsunclassified

Présentation

Baya-Laffite

Benbouzid

2018

socsoc

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S’il est admis que les techniques d’enquête en sociologie — et les modes de raisonnement qui leur sont associés — sont étroitement liées à leurs contextes institutionnels et intellectuels d’apparition, comment le développement des écosystèmes numériques transforme-t-il actuellement les manières de savoir sur le social ? Dans cet article introductif, nous observons la façon dont les sciences sociales computationnelles (SSC) et les humanités numériques mettent en tension la sociologie. D’un côté, les sciences sociales computationnelles concurrencent la sociologie en traitant de ses objets les plus classiques, mais dans une perspective prédictive qui ne lui est pas familière. D’un autre côté, les humanités numériques s’imaginent comme le chapiteau des sciences sociales, mais avec une offre numérique qui s’inscrit dans la tradition méthodologique de l’exégèse textuelle de laquelle les sociologues cherchent à se distancier. La sociologie se trouve comme prise dans l’étau des sciences sociales computationnelles et des humanités numériques. Nous avons observé trois types de réactions face à cette situation — la protection, la conservation et l’adaptation — qui montrent à quel point le numérique pénètre progressivement la discipline sociologique, depuis son propre coeur. Pour que celle-ci devienne pleinement numérique, il convient néanmoins de faciliter les conditions d’accès aux données numériques, notamment en discutant le cadre juridique, économique et technique des écosystèmes numériques de la recherche publique.

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Verstehen for Sociology: Comment on Watts

Cited by 32 publications

References 34 publications

Statistical Modeling: The Three Cultures

Statistical Modeling: The Three Cultures

Almost Confessional: Managing Emotions When Research Breaks Your Heart

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