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Cited by 380

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Explanation and Invariance in the Special Sciences

Woodward, J.·2000·The British Journal for the Philosophy of Science

Abstract

This paper describes an alternative to the common view that explanation in the special sciences involves subsumption under laws. According to this alternative, whether or not a generalization can be used to explain has to do with whether it is invariant rather than with whether it is lawful. A generalization is invariant if it is stable or robust in the sense that it would continue to hold under a relevant if it is stable or robust in the sense that it would continue to hold under a relevant class of changes. Unlike lawfulness, invariance comes in degrees and has other features that are well suited to capture the characteristics of explanatory generalizations in the special sciences. For example, a generalization can be invariant even if it has exceptions or holds only over a limited spatio-temporal interval. The notion of invariance can be used to resolve a number of dilemmas that arise in standard treatments of explanatory generalizations in the special sciences.

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Metadata

Authors
Woodward, J.
Venue
The British Journal for the Philosophy of Science
Published
2000-06-01
Volume
51
Issue
2
Pages
197-254
ISSN
  • 0007-0882 (ISSN-L)
  • 1464-3537
OpenAlex
W2013353303
Cited by
380
References
3
FWCI
125.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Explanation and Invariance in the Special Sciences. https://app.cochat.ai/papers/10.1093/bjps/51.2.197
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Woodward, J. (2000). Explanation and Invariance in the Special Sciences. The British Journal for the Philosophy of Science. https://doi.org/10.1093/bjps/51.2.197

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.