Bibliographic record
Abstract
"Research from the Bank of Canada shows that Canadians use cash for about 30 percent of their purchases in stores, restaurants and other points of sale. We know very little, however, about how Canadians pay their monthly electricity costs, rent, water and other bills. Even less is known about the factors driving consumers' behaviour when paying bills. Yet, bill payments make up a large share of total transactions in Canada. In this paper, I examine how Canadians pay their bills, for example, with a cheque, through online banking or by automatic payment. I use a unique set of 2019 survey data collected from over 4,000 Canadians. Also, I examine why consumers use different options for paying bills. In particular, I look at how consumers’ payment choices and perceptions are influenced by demographics, financial situations, adoption of new technologies, general payment behaviour and bill type. The key conclusion is that there is currently no single, dominant payment method for all consumers and bills. People’s bill payment choices vary with their socio-demographics, attitudes toward new technologies and in-store payment habits. Also, payment options vary widely by bill, and many consumers feel limited in their choices. This suggests that the preferences of billers might play an important role in consumers' options for bill payment. These findings are useful for policy-makers and payment service providers intending to encourage migration away from paper-based payment methods, such as cash or cheques. Indeed, further migration may depend more on the billers than on overcoming consumer resistance to new payment methods."
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".