Bitcoin Awareness and Usage in Canada: <i>An Update</i>
Bibliographic record
Abstract
This article provides an update of the results of the 2017 Bitcoin Omnibus Survey (BTCOS) conducted by the Bank of Canada from December 12 to 15, 2017. The BTCOS was previously conducted in November and December 2016 and the results were reported in Henry, Huynh, and Nicholls (2017, forthcoming). The 2017 survey took place in an interesting time, as Bitcoin prices were increasing and reached an all-time high on December 17, 2017. During this period, the level of awareness of Bitcoin increased from 64 percent in the 2016 BTCOS to 85 percent in the 2017 BTCOS, while ownership rose from 2.9 to 5.0 percent respectively. The main reason cited by survey participants for owning Bitcoin changed from transactional purposes in 2016 to investment purposes in 2017. Further, only about half of Bitcoin owners were found to regularly use Bitcoin to buy goods or services or to send money to other people. TOPICS:Currency, portfolio construction, wealth management
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.026 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".