Abstract
There remains no consensus among social scientists as to how to measure and understand forms of information deprivation such as misinformation. Machine learning and statistical analyses of information deprivation typically contain problematic operationalizations which are too often biased towards epistemic elites’ conceptions that can undermine their empirical adequacy. A mature science of information deprivation should include considerable citizen involvement that is sensitive to the value-ladenness of information quality, and doing so may improve the predictive and explanatory power of extant models.
| Original language | English |
|---|---|
| Pages (from-to) | 1110-1119 |
| Number of pages | 10 |
| Journal | Philosophy of Science |
| Volume | 90 |
| Issue number | 5 |
| Early online date | 16 Feb 2023 |
| DOIs | |
| Publication status | Published - Dec 2023 |
Bibliographical note
Acknowledgments:I thank the following for helpful feedback on previous drafts: Boaz Miller, Emery Neufeld, Michael E. Miller, Mark Peacock, and two anonymous referees. All errors and infelicities are mine alone.