Bibliographic record
Abstract
If New Zealand's EFTPOS networks receive stronger use than similar networks overseas because of differences in price structures what motivations lie behind the structures each has chosen? An analysis of the economic history of networks in New Zealand and a number of other developed countries provides an economic answer to this question. It indicates that it is potential competition between payment networkds for banks and other supply-side participants which promotes efficient networks. government controls that reduce this sort of competition risk harming the development of payment networks and the interests of those that use them.Starting with the introduction of Diner's Club payment card in 1949 the means of payment in the developed world have progressed well beyond the traditional instruments such as notes coins and cheques. Insights can be gained from economic analysis of new retail payment systems in Australia Canada Germany New Zealand Norway and the United Kingdom and United States. Mike uses such analysis to construct a framework to understand the incentives faced by the users of payment instruments and the payment networks that provide them. It also provices a means to assess the role of government in the evolution of retail payment systems.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.054 | 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".