The Evolution and Future of Money in Canada: Implications for the Digital Age, Legal and Regulatory Perspective
Bibliographic record
Abstract
The concept of money in Canada has evolved to adapt to global technological and institutional changes. In this broad context this book explores the impact of emerging digital technologies on how society and government regulators think about money. The Evolution and Future of Money in Canada provides readers a better understanding of the evolving monetary regime in Canada from its early inception to the current emerging digital age. Distinguished legal professor and lawyer Benjamin Geva builds on his expertise in the financial sector to provide a timely study of the Canadian economy. He looks at digital assets such as value-referenced crypto-assets as a potential source of private money, the impact of digital assets on payment systems, and the role of government and central banks in shaping monetary regulations in response to the emergence of digital assets. The Evolution and Future of Money in Canada endeavours to examine Canada’s history and extrapolate insights into our collective future. The book is designed to inform policy development and analysis related to the ongoing financial sector legislative review focused on the digitalization of money. Geva builds on voluminous literature – from Canada and elsewhere – and yet fills a gap in existing research by integrating a legal perspective into a broad policy analysis addressing institutional and technological evolution. Ultimately, this book aims to play an essential role in guiding our future financial policies and legal principles, doctrines, and rules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".