Bibliographic record
Abstract
Payment systems, like plumbing, do not attract much attention when they are working well. A failure, on the other hand, is cause for alarm: as a broken pipe can flood a basement, broken payments can disrupt the financial system. Subpar performance short of a crisis, though less apparent, can also be damaging. Backed-up plumbing causes much inconvenience in familiar ways. A strained payment system also can be very costly. In that regard, Canada’s payment systems need some timely maintenance. A variety of systems make up the Canadian payments landscape: cash, credit cards, debit cards and cheques among others. Most prominent in terms of volume and value are the clearing and settlement systems operated by the Canadian Payments Association (CPA). But the CPA’s systems are now long in the tooth, forcing users to deal with technologies from the 1980s and 1990s. The tremendous advances in information technology since then allow for systems that are faster, cheaper and better able to meet users’ needs. Some countries have already dispensed with paper payments transactions and replaced them with digital payments. An efficient payment system can contribute to the competitiveness of a country’s economy. A first step toward modernizing the Canadian payment system would be replacement of current cheque processing with digital methods. This step alone should save Canadian businesses several billions of dollars per year. It will require reorganizing the CPA’s clearing and settlement systems as a huband-spoke and replacing payee-pull cheques (where the payee’s institution submits the transaction to the settlement system) by payer-push digital payments (where the payer’s financial institution submits the transaction). These steps can be facilitated by a commitment to financing the CPA’s major capital projects through borrowing and recouping the costs through future dues. The success of this modernization will depend on an extensive effort to educate consumers and businesses, especially small businesses, of the benefits of a payer-push electronic payment system. Modernization of the CPA’s payment systems should not stop with eliminating cheques. There is also a need for enhanced information to accompany payments transactions so as to allow seamless end-to-end processing from payer to payee and for real-time processing to limit payment-system risk.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".