Science as a Vocation in the Era of Big Data
The Philosophy of Science behind Big Data and humanity’s Continued Part in Science
Bibliographic Data
| ID | 6071692 |
|---|---|
| Authors | Henrik Skaug Sætra (0000-0002-7558-6451, Østfold University of Applied Sciences, corresponding author) |
| Year | 2018 |
| Volume | 52 |
| Issue | 4 |
| Pages | 508-522 |
| Publication date | 2018-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Integrative Psychological and Behavioral Science (JOURNAL) |
| Journal identifiers | ISSN: 1932-4502 • E-ISSN: 1936-3567 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12124-018-9447-5 |
| PMID | 29974331 |
| OpenAlex | W2820732084 |
| Language | EN |
| Citations received | 13 |
| References cited | 27 |
We now live in the era of big data, and according to its proponents, big data is poised to change science as we know it. Claims of having no theory and no ideology are made, and there is an assumption that the results of big data are trustworthy because it is considered free from human judgement, which is often considered inextricably linked with human error. These two claims lead to the idea that big data is the source of better scientific knowledge, through more objectivity, more data, and better analysis. In this paper I analyse the philosophy of science behind big data and make the claim that the death of many traditional sciences, and the human scientist, is much exaggerated. The philosophy of science of big data means that there are certain things big data does very well, and some things that it cannot do. I argue that humans will still be needed for mediating and creating theory, and for providing the legitimacy and values science needs as a normative social enterprise
Big data · Data science · Epistemology · Humanity · Ideology · Judgement · Legitimacy · Normative · Philosophy of science · Political science · Sociology · Big Data and Business Intelligence · Big Data Technologies and Applications · Computer Science · Law · Philosophy · Scientific Computing and Data Management
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Epistemic Insights as Design Principles for a Teaching-Learning Module on Artificial Intelligence
Scientism, Ethics and Evil
The evolution of data science and big data research
Artificial intelligence, calculative reason, and technical domination
The rise of the research automaton
The autonomous choice architect
To Each Technology Its Own Ethics
A shallow defence of a technocracy of artificial intelligence
A new traditional theory
The Parasitic Nature of Social AI
Exploring the data turn of philosophy of language in the era of big data
The sociology of science
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Business Intelligence and Analytics
Building machines that learn and think like people
Science as a Vocation
Beyond the hype
Critical analysis of Big Data challenges and analytical methods
Creativity and artificial intelligence
Personal Knowledge
‘Hypernudge’
Big data, little history
Big data analytics and the limits of privacy self-management
Objectivity and the Escape from Perspective
Consumption Theory in Terms of Revealed Preference
Method in Social Science
Libertarian Paternalism Is Not an Oxymoron
Critical Questions for Big Data
The Concept of Consciousness
Big Data, new epistemologies and paradigm shifts
Creative Self-Criticism in Science and in Art
| Unique citing works | 13 |
|---|---|
| Citations per year | 1,86 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 11 |