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Abstract This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation.
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Cascade versus Mechanism: The Diversity of Causal Structure in Science
According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term ‘mechanism’ gets regular use in these fields it is unsurprising that philosophers considerit important to scientific explanation. What is surprisingis that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept—it examines the cascade concept in science and the causal structure it refers to. I argue that this conceptis importantly different from the notion of mechanism and that this difference matters for our understanding of causation and explanation in science.
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- Award ID(s):
- 1945647
- PAR ID:
- 10411350
- Date Published:
- Journal Name:
- The British Journal for the Philosophy of Science
- ISSN:
- 0007-0882
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper provides an analysis of explanatory constraints and their role in scientific explanation. This analysis clarifies main characteristics of explanatory constraints, ways in which they differ from “standard” explanatory factors, and the unique roles they play in scientific explanation. While current philosophical work appreciates two main types of explanatory constraints, this paper suggests a new taxonomy: law-based constraints, mathematical constraints, and causal constraints. This classification helps capture unique features of constraint types, the different roles they play in explanation, and it includes causal constraints, which are often overlooked in this literature.more » « less
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During the preschool years, children’s question-explanation exchanges with teachers serve as a powerful mechanism for their early STEM knowledge acquisition. Utilizing naturalistic longitudinal classroom data, we examined how such conversations in an inquiry-based preschool classroom change during an extended scientific inquiry unit. We were particularly interested in information-seeking questions (causal, e.g. “How will you construct a pathway?”; fact-based, e.g., “Where’s the marble?”). Videos (n = 18; 14 hours) were collected during a three-week inquiry unit on forces and motion and transcribed in CLAN-CHILDES software at the utterance level. Utterances were coded for delivery (question vs. statement) and content (e.g., fact-based, causal). Although teachers ask more questions than children, we found a significant increase in information-seeking questions during Weeks 2 and 3. We explored the content of information-seeking questions and found that the majority of these questions were asked by teachers, and focused on facts. However, the timing of fact-based and causal questions varied. Whereas more causal questions occurred in earlier weeks, more fact-based questions were asked towards the end of the inquiry. These findings provide insight into how children’s and teacher’s questions develop during an inquiry, informing our understanding of early science learning. Even in an inquiry-learning environment, teachers guide interactions, asking questions to support children’s learning. Children’s information-seeking questions increase during certain weeks, suggesting that providing opportunities to ask questions may allow children to be more active in constructing knowledge. Such findings are important for considering how science questions are naturally embedded in an inquiry-based learning classroom.more » « less
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Abstract Social scientists appeal to various “structures” in their explanations including public policies, economic systems, and social hierarchies. Significant debate surrounds the explanatory relevance of these factors for various outcomes such as health, behavioral, and economic patterns. This paper provides a causal account of social structural explanation that is motivated by Haslanger (2016). This account suggests that social structure can be explanatory in virtue of operating as a causal constraint, which is a causal factor with unique characteristics. A novel causal framework is provided for understanding these explanations–this framework addresses puzzles regarding the mysterious causal influence of social structure, how to understand its relation to individual choice, and what makes it the main explanatory (and causally responsible) factor for various outcomes.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1086/723623
