F Russo
Biographic Data
| ID | 23826 |
|---|---|
| NAME | F Russo |
| GIVEN NAMES | F |
| FAMILY NAME | Russo |
| SIGNATURE | RUSSO F |
| AFFILIATIONS | University of Amsterdam |
| ORCID | 0000-0002-1993-9697 |
| VERIFIED | Yes |
| TOTAL WORKS | 34 |
| TOTAL CITATIONS | 109 |
| AUTHOR COUNT | 34 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1966 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 4 |
Why Defining Technology Matters. Some Reflections on Radder’s From Commodification to the Common Good: A Synthetic Philosophy of Technology
Situation-Specific Purposes and Epistemic Games: A Framework for Teaching the Evaluation of Causal Information
Studies in science education demonstrate that laypeople typically engage with science to meet situation-specific needs. Their interest in science often emerges only when it directly helps them solve a particular problem. However, most research in science education has focused on evaluating the trustworthiness of information and the level of understanding required for its use. Less attention has been given to assessing the appropriateness of scien…
Conversation About Writing and Citing with Justice and Charity
Citational injustice often refers to the underrepresentation or misrepresentation of the work of members of marginalized populations within academic publications. The literature emphasises how these unjust practices reproduce and perpetuate known hierarchies and colonial power structures. While these are global trends that impact all academic disciplines, a full understanding of them requires considering how these practices play out within specif…
Interconnected Health-Environmental Challenges: The Implosion of the Modernist Evidence Regime and the Need of Pluralist Evidence Practices
Increased pollution, obesity rates, or the COVID-19 pandemic are only a few examples of the numerous intertwined health-environmental challenges humanity is facing. The severity of these challenges strongly suggests that research in these fields is failing to generate evidence to support decisions and actions that can help address, mitigate or adapt to them. In this article, we look into some of the underlying assumptions underpinning mainstream …
Epistemic Challenges Faced by Non-native English Speakers in Philosophy: Evidence from an International Survey
The widespread use of English in the field of philosophy facilitates international collaboration but may also pose significant challenges in understanding, analyzing, or producing information for both native (NES) and non-native English speakers (NNES). These challenges have not yet been systematically investigated. We conducted an international survey of philosophers ( N = 1,615), comparing NES and NNES, while controlling for their academic posi…
A Network Approach to Public Trust in Generative AI
As generative AI becomes more deeply integrated into society, building public trust in this technology has emerged as a key challenge for policymakers. Existing approaches, such as the European Commission’s Trustworthy AI framework, largely seek to tackle this issue by offering comprehensive technical and legal measures for promoting a more trustworthy AI industry. However, this paper argues that such approaches are limited in scope and do not fu…
Where to after Covid-19? Systems thinking for a human-centred approach to pandemics
The COVID-19 pandemic was the biggest public health crisis that the world experienced on a global scale in recent history. It exposed systemic weaknesses and fragilities in health, economic, political, environmental and social systems (Haley, Paucar-Caceres, and Schlindwein, 2021 ). Since the early days of the crisis, governments around the world sought evidence-based management strategies, turning to science to inform decisions (Yu et al., 2021 …
Connecting ethics and epistemology of AI
The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemo…
Synthetic Socio-Technical Systems: Poiêsis as Meaning Making
With the recent renewed interest in AI, the field has made substantial advancements, particularly in generative systems. Increased computational power and the availability of very large datasets has enabled systems such as ChatGPT to effectively replicate aspects of human social interactions, such as verbal communication, thus bringing about profound changes in society. In this paper, we explain that the arrival of generative AI systems marks a s…
How is who: Evidence as clues for action in participatory sustainability science and public health research
Participatory and collaborative approaches in sustainability science and public health research contribute to co-producing evidence that can support interventions by involving diverse societal actors that range from individual citizens to entire communities. However, existing philosophical accounts of evidence are not adequate to deal with the kind of evidence generated and used in such approaches. In this paper, we present an account of evidence…
Practical wisdom and virtue ethics for knowledge co-production in sustainability science
Promoting the health of vulnerable populations: Three steps towards a systems-based re-orientation of public health intervention research
This paper proposes a novel framework for the development of interventions in vulnerable populations. The framework combines a complex systems lens with syndemic theory. Whereas funding bodies, research organizations and reporting guidelines tend to encourage intervention research that (i) focuses on singular and predefined health outcomes, (ii) searches for generalizable cause-effect relationships, and (iii) aims to identify universally effectiv…
Time and causality in the social sciences
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 …
Covid-19 heralds a new epistemology of science for the public good
Experimental practices and objectivity in the social sciences: Re-embedding construct validity in the internal–external validity distinction
The experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the ‘quantitative revolution’ in the nineteenth century. One of the often valued features of social science experimentation is precisely the fact that there are (alleged) clear methodological rules regarding hypothesis testing that come from the methods of the natural sciences and from the methodology of RCTs in the…
Digital Technologies, Ethical Questions, and the Need of an Informational Framework
Technologies have always been bearers of profound changes in science, society, and any other aspect of life. The latest technological revolution-the digital revolution-is no exception in this respect. This paper presents the revolution brought about by digital technologies through the lenses of a specific approach: the philosophy of information. It is argued that the adoption of an informational approach helps avoiding utopian or dystopian approa…
The Day the Milk Went Sour: Bridging Epistemology and Ontology in Latour’s Empirical Philosophy
Causal attribution in block-recursive social systems: A structural modeling perspective
One method for causal analysis in the social sciences is structural modeling. Structural models, as used in this article, model the (causal) mechanism for a social phenomenon by recursively decomposing the multivariate distribution of the variables of interest. Often, however, one does not achieve a complete decomposition in terms of single variables but in terms of "blocks" of variables only. Papers giving an overview of this issue are neverthel…
Causal narratives in public health: The Difference Between Mechanisms of Aetiology and Mechanisms of Prevention in Non-Communicable Diseases
Research in the health sciences has been highly successful in revealing the aetiologies of many morbidities, particularly those involving the microbiology of communicable disease. This success has helped form a narrative to be found in numerous public health documents, about interventions to reduce the burden of non-communicable diseases (e.g., obesity or alcohol related pathologies). These focus on tackling the purported pathogenic factors causi…
Causation in Mixed Methods Research: The Meeting of Philosophy, Science, and Practice
This article provides a systematic and pluralistic theory of causation that fits the kind of reasoning commonly found in mixed methods research. It encompasses a variety of causal concepts, notions, approaches, and methods. Each instantiation of the theory is like a mosaic, where the image appears when the tiles are appropriately displayed. This means that researchers should carefully construct a causal mosaic for each research study, articulatin…
Controlling Variables in Social Systems - A Structural Modelling Approach
Determining the variables to be controlled for is usually a major problem in the social sciences when analyzing possible causal relations. A structural modelling approach, having recourse to directed acyclic graphs, is presented here as a consistent framework for determining a coherent set of guidelines when deciding what variables should be controlled. Two rules are developed for determining control variables when studying respectively the direc…
D’abord les données, ensuite la méthode: Big data et déterminisme en sciences sociales
While large quantities of data have long been used by social science researchers, for example survey questionnaires, the use of massive and heterogeneous digital data, or “big data” is more and more frequent. As theory is abandoned in the search for correlations, is this multitude of data promoting a new form of determinism? On the contrary, the history of the social sciences demonstrates that the increase in data available has led to a gradual p…
Critical data studies: An introduction
Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact wi…
Mechanisms and the Evidence Hierarchy
Philosophy of medicine: Between clinical trials and mechanisms
Critical data studies: An introduction
Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact wi…
Do We Necessarily Need Longitudinal Data to Infer Causal Relations
A-t-on nécessairement besoin de données longitudinales pour inférer des relations causales ? Il est généralement admis que les causes précèdent leurs effets dans le temps. Cela justifie usuellement la préférence pour les études longitudinales par rapport aux études transversales, parce que les premières permettent la modèlisation du processus dynamique engendrant le résultat, tandis que les secondes ne le peuvent pas. Les partisans de l’approche …
Causation in Mixed Methods Research: The Meeting of Philosophy, Science, and Practice
This article provides a systematic and pluralistic theory of causation that fits the kind of reasoning commonly found in mixed methods research. It encompasses a variety of causal concepts, notions, approaches, and methods. Each instantiation of the theory is like a mosaic, where the image appears when the tiles are appropriately displayed. This means that researchers should carefully construct a causal mosaic for each research study, articulatin…
Causal narratives in public health: The Difference Between Mechanisms of Aetiology and Mechanisms of Prevention in Non-Communicable Diseases
Research in the health sciences has been highly successful in revealing the aetiologies of many morbidities, particularly those involving the microbiology of communicable disease. This success has helped form a narrative to be found in numerous public health documents, about interventions to reduce the burden of non-communicable diseases (e.g., obesity or alcohol related pathologies). These focus on tackling the purported pathogenic factors causi…
Causal attribution in block-recursive social systems: A structural modeling perspective
One method for causal analysis in the social sciences is structural modeling. Structural models, as used in this article, model the (causal) mechanism for a social phenomenon by recursively decomposing the multivariate distribution of the variables of interest. Often, however, one does not achieve a complete decomposition in terms of single variables but in terms of "blocks" of variables only. Papers giving an overview of this issue are neverthel…
Controlling Variables in Social Systems - A Structural Modelling Approach
Determining the variables to be controlled for is usually a major problem in the social sciences when analyzing possible causal relations. A structural modelling approach, having recourse to directed acyclic graphs, is presented here as a consistent framework for determining a coherent set of guidelines when deciding what variables should be controlled. Two rules are developed for determining control variables when studying respectively the direc…
Inferring Causality through Counterfactuals in Observational Studies - Some Epistemological Issues
L’inférence causale par contrefactuels dans les études observationnelles — Quelques épistémologiques : Cet article contribue au débat sur les vertus et les vices de contrefactuels comme base pour l’inférence causale. L’objectif est de mettre l’approche contrefactuelle dans une perspective épistémologique. Nous discutons d’un certain nombre de questions, allant de sa base non observable au parallélisme établi entre cette approche en statistique et…
Practical wisdom and virtue ethics for knowledge co-production in sustainability science
Experimental practices and objectivity in the social sciences: Re-embedding construct validity in the internal–external validity distinction
The experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the ‘quantitative revolution’ in the nineteenth century. One of the often valued features of social science experimentation is precisely the fact that there are (alleged) clear methodological rules regarding hypothesis testing that come from the methods of the natural sciences and from the methodology of RCTs in the…
A Study of Bias in TV Coverage of the Vietnam War: 1969 And 1970
Journal Article A STUDY OF BIAS IN TV COVERAGE OF THE VIETNAM WAR: 1969 AND 1970 Get access FRANK D. RUSSO FRANK D. RUSSO Frank D. Russo is a student at Yale University. Search for other works by this author on: Oxford Academic Google Scholar Public Opinion Quarterly, Volume 35, Issue 4, WINTER 1971, Pages 539–543, https://doi.org/10.1086/267949 Published: 01 January 1971
Are Causal Analysis and System Analysis Compatible Approaches
In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only ‘closed’ and ‘hermetic’ systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be comp…
Le phénomène urbain
A Study of Bias in TV Coverage of the Vietnam War: 1969 And 1970
Journal Article A STUDY OF BIAS IN TV COVERAGE OF THE VIETNAM WAR: 1969 AND 1970 Get access FRANK D. RUSSO FRANK D. RUSSO Frank D. Russo is a student at Yale University. Search for other works by this author on: Oxford Academic Google Scholar Public Opinion Quarterly, Volume 35, Issue 4, WINTER 1971, Pages 539–543, https://doi.org/10.1086/267949 Published: 01 January 1971
A pedostratigraphic marker in the geomorphological evolution of the Campanian Apennines (Southern Italy): The Paleosol of Eboli
Interpreting Causality in the Health Sciences
We argue that the health sciences make causal claims on the basis of evidence both of physical mechanisms, and of probabilistic dependencies. Consequently, an analysis of causality solely in terms of physical mechanisms or solely in terms of probabilistic relationships, does not do justice to the causal claims of these sciences. Yet there seems to be a single relation of cause in these sciences—pluralism about causality will not do either. Instea…
Causality and Causal Modelling in the Social Sciences
Do We Necessarily Need Longitudinal Data to Infer Causal Relations
A-t-on nécessairement besoin de données longitudinales pour inférer des relations causales ? Il est généralement admis que les causes précèdent leurs effets dans le temps. Cela justifie usuellement la préférence pour les études longitudinales par rapport aux études transversales, parce que les premières permettent la modèlisation du processus dynamique engendrant le résultat, tandis que les secondes ne le peuvent pas. Les partisans de l’approche …
Are Causal Analysis and System Analysis Compatible Approaches
In social science, one objection to causal analysis is that the assumption of the closure of the system makes the analysis too narrow in scope, that is, it considers only ‘closed’ and ‘hermetic’ systems thus neglecting many other external influences. On the contrary, system analysis deals with complex structures where every element is interrelated with everything else in the system. The question arises as to whether the two approaches can be comp…
Causal Webs in Epidemiology
The notion of "causal web" emerged in the epidemiological literature in the early Sixties and had to wait until the Nineties for a thorough critical appraisal. Famously, Nancy Krieger argued that such a notion isn't helpful unless we specify what kind of spiders create the web. This means, according to Krieger, (i) that the role of the spiders is to provide an explanation of the yarns of the web and (ii) that the sought spiders have to be biologi…
Inferring Causality through Counterfactuals in Observational Studies - Some Epistemological Issues
L’inférence causale par contrefactuels dans les études observationnelles — Quelques épistémologiques : Cet article contribue au débat sur les vertus et les vices de contrefactuels comme base pour l’inférence causale. L’objectif est de mettre l’approche contrefactuelle dans une perspective épistémologique. Nous discutons d’un certain nombre de questions, allant de sa base non observable au parallélisme établi entre cette approche en statistique et…
Philosophy of medicine: Between clinical trials and mechanisms
Mechanisms and the Evidence Hierarchy
Controlling Variables in Social Systems - A Structural Modelling Approach
Determining the variables to be controlled for is usually a major problem in the social sciences when analyzing possible causal relations. A structural modelling approach, having recourse to directed acyclic graphs, is presented here as a consistent framework for determining a coherent set of guidelines when deciding what variables should be controlled. Two rules are developed for determining control variables when studying respectively the direc…
D’abord les données, ensuite la méthode: Big data et déterminisme en sciences sociales
While large quantities of data have long been used by social science researchers, for example survey questionnaires, the use of massive and heterogeneous digital data, or “big data” is more and more frequent. As theory is abandoned in the search for correlations, is this multitude of data promoting a new form of determinism? On the contrary, the history of the social sciences demonstrates that the increase in data available has led to a gradual p…
Critical data studies: An introduction
Critical Data Studies (CDS) explore the unique cultural, ethical, and critical challenges posed by Big Data. Rather than treat Big Data as only scientifically empirical and therefore largely neutral phenomena, CDS advocates the view that Big Data should be seen as always-already constituted within wider data assemblages. Assemblages is a concept that helps capture the multitude of ways that already-composed data structures inflect and interact wi…
Causation in Mixed Methods Research: The Meeting of Philosophy, Science, and Practice
This article provides a systematic and pluralistic theory of causation that fits the kind of reasoning commonly found in mixed methods research. It encompasses a variety of causal concepts, notions, approaches, and methods. Each instantiation of the theory is like a mosaic, where the image appears when the tiles are appropriately displayed. This means that researchers should carefully construct a causal mosaic for each research study, articulatin…
Digital Technologies, Ethical Questions, and the Need of an Informational Framework
Technologies have always been bearers of profound changes in science, society, and any other aspect of life. The latest technological revolution-the digital revolution-is no exception in this respect. This paper presents the revolution brought about by digital technologies through the lenses of a specific approach: the philosophy of information. It is argued that the adoption of an informational approach helps avoiding utopian or dystopian approa…
The Day the Milk Went Sour: Bridging Epistemology and Ontology in Latour’s Empirical Philosophy
Causal attribution in block-recursive social systems: A structural modeling perspective
One method for causal analysis in the social sciences is structural modeling. Structural models, as used in this article, model the (causal) mechanism for a social phenomenon by recursively decomposing the multivariate distribution of the variables of interest. Often, however, one does not achieve a complete decomposition in terms of single variables but in terms of "blocks" of variables only. Papers giving an overview of this issue are neverthel…
Causal narratives in public health: The Difference Between Mechanisms of Aetiology and Mechanisms of Prevention in Non-Communicable Diseases
Research in the health sciences has been highly successful in revealing the aetiologies of many morbidities, particularly those involving the microbiology of communicable disease. This success has helped form a narrative to be found in numerous public health documents, about interventions to reduce the burden of non-communicable diseases (e.g., obesity or alcohol related pathologies). These focus on tackling the purported pathogenic factors causi…
Covid-19 heralds a new epistemology of science for the public good
Experimental practices and objectivity in the social sciences: Re-embedding construct validity in the internal–external validity distinction
The experimental revolution in the social sciences is one of the most significant methodological shifts undergone by the field since the ‘quantitative revolution’ in the nineteenth century. One of the often valued features of social science experimentation is precisely the fact that there are (alleged) clear methodological rules regarding hypothesis testing that come from the methods of the natural sciences and from the methodology of RCTs in the…
Time and causality in the social sciences
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 …
Practical wisdom and virtue ethics for knowledge co-production in sustainability science
Promoting the health of vulnerable populations: Three steps towards a systems-based re-orientation of public health intervention research
This paper proposes a novel framework for the development of interventions in vulnerable populations. The framework combines a complex systems lens with syndemic theory. Whereas funding bodies, research organizations and reporting guidelines tend to encourage intervention research that (i) focuses on singular and predefined health outcomes, (ii) searches for generalizable cause-effect relationships, and (iii) aims to identify universally effectiv…
Where to after Covid-19? Systems thinking for a human-centred approach to pandemics
The COVID-19 pandemic was the biggest public health crisis that the world experienced on a global scale in recent history. It exposed systemic weaknesses and fragilities in health, economic, political, environmental and social systems (Haley, Paucar-Caceres, and Schlindwein, 2021 ). Since the early days of the crisis, governments around the world sought evidence-based management strategies, turning to science to inform decisions (Yu et al., 2021 …
Philosophy (18 works) · Epistemology (17 works) · Computer Science (15 works) · Sociology (13 works) · Psychology (10 works) · Philosophy of science (9 works) · Engineering (8 works) · Engineering ethics (7 works) · Medicine (7 works) · Philosophy (7 works)