Bibliographic record
Abstract
Bitcoin, digital currencies and FinTech have been the subject of vigorous discussion. There has, however, been limited empirical evidence of its adoption and usage. This paper proposes a methodology to collect a nationally representative sample via the Bitcoin Omnibus Survey (BTCOS) in order to track the ubiquity and usage of Bitcoin in Canada. The paper reveals that about 64 per cent of Canadians have heard of Bitcoin, but only 2.9 per cent own it. Awareness of Bitcoin is strongly associated with men, and those with college or university education; additionally, Bitcoin awareness is more concentrated among unemployed individuals. On the other hand, Bitcoin ownership is associated with younger age groups and a high school education. Furthermore, the current authors have constructed a test of Bitcoin characteristics to attempt to gauge the level of knowledge held by respondents who were aware of Bitcoin, including actual owners. Knowledge is positively correlated with Bitcoin adoption. This paper attempts to reconcile the difference in awareness and ownership by deconstructing the transaction and store-of-value motive for holding Bitcoin. The paper concludes with some suggestions to improve future digital currency surveys, in particular to achieve precise estimates from the hard-to-reach population of digital currency users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".