Abstract
This comment argues that the position statement by Aksoy et al. (2026) articulates necessary but insufficient conditions for disciplinary standards in sociology. Integrating theory and empirics, open science practices, and engagement with pressing social issues are essential requirements; yet they leave unaddressed the capacity of sociological knowledge to produce operationally testable effects. Drawing on Prasad’s distinction between the causes of problems and the causes of solutions, the paper contends that analytical robustness must also be assessed through what happens when knowledge performs, when it must orient decisions, reveal resistances, and enable collective learning. The pragmatist tradition offers a complementary reframing: a sociological claim is not fully robust until it survives the test of application and generates effects that can be learned from. The comment then develops an analogy with medical research to articulate what might be called a standard of “clinical causality”. Just as preclinical findings may be causally well-identified yet therapeutically irrelevant, sociological mechanisms may pass all internal tests while remaining fragile, non-transferable, or operationally vacuous. Unlike clinical trials, however, social interventions cannot rely on increasing control to achieve transferability; they require instead an adaptive mix of control and learning, in which failure is treated as data about the limits of the theory rather than a shortcoming of implementation. An expanded standard of this kind look would preserve everything Aksoy et al. advocate while adding a fourth evaluative dimension: the capacity to translate knowledge into solutions that are testable, transferable, and capable of generating cumulative learning from their effects.
Keywords
Explanation and problem-solving
The position statement by Aksoy et al. (2026) articulates three key principles for disciplinary standards in sociology: integration of theory and empirics, open science practices, and engagement with pressing social issues. Yet, as I will contend, the statement remains incomplete if the robustness of sociological knowledge is established independently of its operational consequences.
Consider the case of social contagion illustrated by Watts (2017). Two competing theoretical frameworks — threshold models and information cascades — meet the standards invoked by Aksoy et al. Yet applying a threshold model to a problem governed by information cascades (or vice versa) risks producing weak, ineffective, and even counterproductive interventions. In this scenario, what is needed is a comparative test of the models’ capacity to solve problems in the contexts where they must operate, to predict effects across different settings, and to remain causally effective when conditions change. Here lies what Monica Prasad calls “the causes of solutions” as distinct from “the causes of the problem” (Prasad 2021a).
Aksoy et al. further note that sociology remains divided on its positioning between the scientific method versus activism (Rubin 2025). This framing implicitly implies that explaining social problems and making sense of solutions of collective interest are separable operations. The pragmatist tradition (Prasad 2021b) begs to differ: a belief is robust not just when it passes internal standards, but also when it generates effects that can be learned from. The test of whether a theory is sound should include research questions such as: does it enable practitioners, policymakers, and communities to anticipate obstacles to change? Does it reveal the resistances that emerge when solutions are attempted? (Goldthorpe 2025). The key issue here is that the consequences of application are not external tests of an otherwise solid knowledge, rather they are part of the truth-seeking process itself.
Second, the generative attrition between internal standards and external efficacy sheds light on the thorny issue of transferability and durability of causal mechanisms across contexts. Aksoy et al. rightly emphasize generative causality as a key standard, but we all know that a mechanism working in one context may fail to transfer to another and that effects may be fragile in real-world settings. This would require sociologists to interrogate under what conditions the causal pathway remains stable, how effects scale across populations, and what contextual variations are key (Boudon 2014).
Third, operational efficacy would feed a more productive distinction between academic work and political activism. Aksoy et al. are correct to distinguish these domains and to insist that sociological analysis must be governed by its own standards. The distinction becomes problematic when it is used to wall off knowledge production from the public assessment of knowledge itself. Sociology can, and indeed should, have analytical independence from partisan agendas in a narrow sense while simultaneously remaining accountable for whether its findings, once applied, produces effects that are amenable to collective learning and evaluation.
An expanded standard perspective of this kind look would preserve everything Aksoy et al. advocate and it would add the capacity to translate knowledge into solutions capable of generating collective learning. This does not require every sociologist to be an applied researcher, but it does require that the discipline as such create safe spaces for practice and learning.
A plea for clinical causality
In this perspective, sociology can learn much from medical research (Ioannids 2005). In preclinical research, the analysis of causal relationships occurs through extremely precise and controlled experimental designs. However, most identified mechanisms produce impacts that are too small, unstable, or poorly transferable to be clinically relevant. Causality, in these cases, is robust but therapeutically irrelevant. Analogously, causal identification provides limited information about the magnitude, stability, and transferability of observed effects (Deaton and Cartwright 2018). The kind of applied and explanatory sociology I am arguing for confronts a problem analogous to that of preclinical research. It is not enough to know that an intervention works in the laboratory, in a data space or in whatever kind of formal/mathematical/agent-based model. When attention to causal identification and generative sufficiency obscures substantive relevance, “causal estimands”, as invoked by Askoy et al. represents a necessary but not sufficient condition.
This has both applied and theoretical implications. Unlike systems where the transition from preclinical to clinical level typically relies on increased control over relevant variables and parameters, in social systems this transition requires a variable mix of control and learning. When a drug moves from preclinical to clinical phase, the key aim is to better control dosage, administration, interactions with other active ingredients, side effects and so on and so forth. Greater control means greater reliability of effect through reduction of margins of uncertainty. In social systems, however, people react, interpret, adapt their behaviors, and modify the rules of the game. A clinically relevant intervention in the social domain incorporates mechanisms for recognizing uncertainty and variability, modifying itself accordingly (Sabel et al. 2024). In applied sociological theory transferability depends on the possibility of transferring the causal capacities identified (Cartwright 1989). How this involves the role of learning through effects is exactly part of the “causes of the solution” to address.
The European Academy of Sociology, by embracing this expanded vision of standards, would be perceived not merely as the guardian of disciplinary excellence in the name of a pre-clinical knowledge, but as a force for making sociology “really-really” matters (Barbera 2025; Boudon 2002) and capable of learning from the world as it seeks to explain it.
Footnotes
Ethical considerations
This submission consists of a theoretical and methodological position statement and does not involve human subjects research or data collection requiring ethical review.
Consent to participate
This submission does not involve human subjects or the collection of personal data.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
This submission is a theoretical contribution and does not report empirical data requiring archival or accessibility statements.
