Bibliographic record
Abstract
The emergence of a range of digital media of exchange such as debit cards, credit cards, pre-loaded transport tickets, cafeteria passes and telephone coupons means for the first time that money in the form of paper currency and coins is no longer essential. The scrapping of traditional cash would mean the removal of the means of anonymous exchange with implications for the fiscal deficit. On the one hand, it would mean a loss of seigniorage for national governments, with the size of this loss depending on how the cash is removed from circulation. On the other hand, it would reduce tax evasion, which would increase fiscal revenue of national, state and municipal governments. The absence of an anonymous means of exchange also has favourable fiscal implications from reduced spending on law enforcement, legal proceedings, incarceration, and public health from impeding the drug trade: digital media of exchange provide a trail to follow for illicit transactions, hindering activities of buyers and sellers. This paper calculates the size of the fiscal implications of scrapping cash and finds substantial gains for all levels of a nation’s government, even the national government that loses seigniorage. It takes the view of Canada where virtually every adult has an account at a financial institution.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".