The Ontology of Patterns in Empirical Data
Bibliographic Data
| ID | 10706151 |
|---|---|
| Authors | James W Mcallister (corresponding author) |
| Year | 2010 |
| Volume | 77 |
| Issue | 5 |
| Pages | 804-814 |
| Publication date | 2010-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0031-8248 • E-ISSN: 1539-767X |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1086/656555 |
| OpenAlex | W2083388627 |
| Language | EN |
| Citations received | 5 |
| References cited | 12 |
This article defends the following claims. First, for patterns exhibited in empirical data, there is no criterion on which to demarcate patterns that are physically significant and patterns that are not physically significant. I call a pattern physically significant if it corresponds to a structure in the world. Second, all patterns must be regarded as physically significant. Third, distinct patterns must be regarded as providing evidence for distinct structures in the world. Fourth, in consequence, the world must be conceived as showing all possible structures
Data science · Empirical evidence · Epistemology · Ontology · Biomedical Text Mining and Ontologies · Computer Science · Data Visualization and Analytics · Philosophy · Philosophy and History of Science
What do patterns in empirical data tell us about the structure of the world
Data and phenomena
Algorithmic randomness in empirical data
Model Selection and the Multiplicity of Patterns in Empirical Data
Saving the Phenomena” Today
Autonomous Patterns and Scientific Realism
Noise in the World
Data, Phenomena, Signal, and Noise
Saving the Phenomena
Fact, fiction, and forecast
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,31 |
| Citation span | 2010 - 2023 (14) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 5 |