The security and financial implications of blockchain technologies: Regulating emerging technologies in Canada
Bibliographic record
Abstract
Driven by advances in data analytics, machine learning, and smart devices, financial technology is changing the way Canadians interact with the financial sector. The evolving landscape is further influenced by cryptocurrencies: non-fiat, decentralized digital payment systems, like Bitcoin, that operate outside the formal financial sector. While Bitcoin has garnered attention for facilitating criminal activity, including money laundering, terrorism financing, digital ransomware, weapons trafficking, and tax evasion, it is Bitcoin's underlying protocol, the blockchain, that represents an innovation capable of transforming financial services and challenging existing security, financial, and public safety regulations and policies. Canada's challenge is to find the right balance between oversight and innovation. Our paper examines these competing interests: we provide an overview of blockchain technologies, illustrate their potential in Canada and abroad, and examine the government's role in fostering innovation while concurrently bolstering regulations, maintaining public safety, and securing the integrity of financial systems.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".