Comparing Fields of Sciences: Multilevel Networks of Research Collaborations in Italian Academia

Bellotti, Elisa; Guadalupi, Luigi; Conaldi, Guido

doi:10.1007/978-3-319-24520-1_9

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…Network autocorrelation models use correlation structures to represent dependencies between the values of linked actors. In this volume, they are used in the contributions by Agneessens and Koskinen (2015) and Bellotti et al (2015). Another way to model this was proposed by Tranmer et al (2014), who used the multiple membership models of Browne et al (2001) to represent network effects on individual outcomes.…”

Section: The Basic Multilevel Nature Of Social Network Analysismentioning

confidence: 99%

The Multiple Flavours of Multilevel Issues for Networks

Snijders¹

2015

Multilevel Network Analysis for the Social Sciences

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Section: The Basic Multilevel Nature Of Social Network Analysismentioning

confidence: 99%

The Multiple Flavours of Multilevel Issues for Networks

Snijders¹

2015

Multilevel Network Analysis for the Social Sciences

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“…For example, scientists collaborate with others, and these networks are embedded in disciplinary and organizational levels. This relational structure of scientific collaboration plays a role in the scholars' success, that is, the funding they receive (Bellotti et al, 2016).…”

Section: Causalitymentioning

confidence: 99%

Time and causality in the social sciences

et al. 2021

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This article deals with the role of time in causal models in the social sciences. The aim is to underline the importance of time-sensitive causal models, in contrast to time-free models. The relation between time and causality is important, though a complex one, as the debates in the philosophy of science show. In particular, an outstanding issue is whether one can derive causal ordering from time ordering. The article examines how time is taken into account in demography and in economics as examples of social sciences in which considerations about time may diverge. We then present structural causal modeling as a modeling strategy that, while not essentially based on temporal information, can incorporate it in a more or less explicit way. In particular, we argue that temporal information is useful to the extent that it is placed in a correct causal structure, thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant than the temporal order for explanatory purposes, in establishing causal ordering the researcher should nevertheless take into account the time-patterns of causes and effects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models.

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“…This relational structure of scientic collaboration plays a role in the scholars' success, i.e. the funding they receive (Bellotti et al, 2016).…”

Section: Causalitymentioning

confidence: 99%

Time and Causality in the Social Sciences

Wunsch¹,

Russo

Mouchart

et al. 2020

SSRN Journal

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This article deals with the role of time in causal models in the social sciences, in particular in structural causal modeling, in contrast to time-free models. The aim is to underline the importance of time-sensitive causal models. For this purpose, it also refers to the important discussion on time and causality in the philosophy of science, and examines how time is taken into account in demography and in economics as examples of social sciences. Temporal information is useful to the extent that it is placed in a correct causal structure, and thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant for explanatory purposes than the temporal order, the former should nevertheless take into account the time-patterns of causes and eects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models.

show abstract

Comparing Fields of Sciences: Multilevel Networks of Research Collaborations in Italian Academia

Cited by 7 publications

References 21 publications

The Multiple Flavours of Multilevel Issues for Networks

The Multiple Flavours of Multilevel Issues for Networks

Time and causality in the social sciences

Time and Causality in the Social Sciences

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