Bibliographic record
Abstract
Policymaking circles and central banks around the world are now giving serious consideration to the pros and cons of making central bank digital currencies (CBDCs) available to the general public. While the consensus view remains that such a move would be premature, opinion appears to be shifting. Indeed, developments in a number of advanced and emerging economies indicate that the CBDC model is receiving more serious consideration than it has in the past. The numerous speeches and research papers coming from central banks are testament to this growing interest. Moreover, some countries and central banks have moved beyond talking and have taken active steps to push the initiative further. Proponents view the introduction of CBDCs as a potentially positive development rather than a purely defensive reaction. Indeed, they believe CBDCs could materially improve the role of central bank money in the financial system by providing a more stable unit of account, a more efficient medium of exchange and a more secure store of value. Moreover, the potential benefits go well beyond these traditional central bank money functions. Proponents suggest that CBDCs could temper financial instability, improve the implementation and transmission of monetary policy, raise productivity, help finance government deficits, reduce tax evasion and discourage a number of other costly and illegal activities. These positive claims have not gone unchallenged. The most common concern raised is the destabilizing effect that CBDCs might have on the economy in times of financial stress. As a safe and convenient alternative to commercial bank deposits and other types of private financial assets, CBDCs might act as a dangerous accelerant in the context of a bank run, transforming an isolated concern about one bank’s solvency into a system-wide crisis. Another source of concern is the disruptive effect that CBDCs would likely have on the competitive position of commercial banks, other financial institutions and key financial market infrastructures. In the end, the best way forward for Canada and other countries may not involve the introduction of a CBDC. Some active government engagement now would nevertheless seem advisable to ensure the most promising ways forward are not precluded. Simply leaving it to the market to sort out would be very risky. The disruption caused by any policy reversals that might be contemplated at a later stage could prove insurmountable, leaving us in a place we would rather not be. It is important to understand that maintaining the status quo is unlikely to be a practicable option, given the shifting financial landscape. The question is not whether central banks will need to react, but how they should react to these tectonic technological shocks.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".