Économies d’échelle dans les opérations des caisses populaires du district de Québec
Bibliographic record
Abstract
The application of the computer to the servicing of deposit accounts at banks and non-bank financial intermediaries is a fairly recent development. Most empirical studies of economies of scale in this industry date prior to this technological transition. There is one notable exception, however, and that is the study by D. L. Daniel, W. A. Longbrake and N. B. Murphy (1972), in which they reported economies of scale in the servicing of checking deposits for computerized banks, especially when the number of such accounts exceeds the 10,600 mark. The present study examines the issue for a different type of computer-using deposit institution, namely: a sample of 128 Canadian Credit Unions located in the district of Quebec and referred to as the "Caisses Populaires" (C.P.'s). These institutions were chosen for the study because they present some unique characteristics and also because they were among the first Canadian financial institutions to computerize the servicing of their deposit operations. Following G. J. Benston (1970) and F. Bell and N. Murphy (1968), the data has been tested using two different models. The empirical results of both tests indicate that computerization didnotgenerate any economies of scale in the checking deposit accounts. Further analysis reveals that the potential economies of scale were captured by the lessor of the equipment through a financial arrangement tying the rent to the number of cheking accounts to be serviced.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".