Bitcoin Awareness and Usage in Canada: An Update
Bibliographic record
Abstract
To help monitor the adoption and use of Bitcoin, the Bank of Canada conducted a survey in late 2016 and then again in late 2017. The second survey took place during an interesting time, since Bitcoin prices were increasing and reached an all-time high on December 17, 2017. The level of awareness of Bitcoin rose from 64 per cent in the 2016 Bitcoin Omnibus Survey (BTCOS) to 85 per cent in the 2017 BTCOS, while ownership rose from 2.9 to 5.0 per cent. The main reason survey participants cited for owning Bitcoin changed from transactional purposes in 2016 to investment purposes in 2017. In addition, only about half of Bitcoin owners were regularly using Bitcoin to buy goods or services or to send money to other people. This provides empirical support for the notion that most of the fluctuations in the Bitcoin price are due to speculation. However, self-reported holdings of Bitcoin among users did not change remarkably during the period between surveys.
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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 | 0.001 |
| 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".