The political, psychological, and social correlates of cryptocurrency ownership
Bibliographic record
Abstract
Cryptocurrency is a digital asset secured by cryptography that has become a popular medium of exchange and investment known for its anonymous transactions, unregulated markets, and volatile prices. Given the popular subculture of traders it has created, and its implications for financial markets and monetary policy, scholars have recently begun to examine the political, psychological, and social characteristics of cryptocurrency investors. A review of the existing literature suggests that cryptocurrency owners may possess higher-than-average levels of nonnormative psychological traits and exhibit a range of non-mainstream political identities. However, this extant literature typically employs small nonrepresentative samples of respondents and examines only a small number of independent variables in each given study. This presents the opportunity for both further testing of previous findings as well as broader exploratory analyses including more expansive descriptive investigations of cryptocurrency owners. To that end, we polled 2,001 American adults in 2022 to examine the associations between cryptocurrency ownership and individual level political, psychological, and social characteristics. Analyses revealed that 30% of our sample have owned some form of cryptocurrency and that these individuals exhibit a diversity of political allegiances and identities. We also found that crypto ownership was associated with belief in conspiracy theories, "dark" personality characteristics (e.g., the "Dark Tetrad" of narcissism, Machiavellianism, psychopathy, and sadism), and more frequent use of alternative and fringe social media platforms. When examining a more comprehensive multivariate model, the variables that most strongly predict cryptocurrency ownership are being male, relying on alternative/fringe social media as one's primary news source, argumentativeness, and an aversion to authoritarianism. These findings highlight numerous avenues for future research into the people who buy and trade cryptocurrencies and speak to broader global trends in anti-establishment attitudes and nonnormative behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".