Bank Credit Product Quality Standartisation: Necessity and Accomplishment
Bibliographic record
Abstract
The article looks into possibility and necessity of bank credit product standartisation. Apparent reasoning for developing economies advocates standartisation as cushy method of assuring adequate quality to any bank product while in actual practice no such methods are implemented allegedly due to peculiarities of bank “manufacturing” process and specifics of bank credit products consumption. This article presents analysis of such impediments and ways of their elimination. In respect with existing methods of assessing bank service quality level such as SERVQUAL and SERVPERF author explores their applicability to bank credit product and offers a user-friendly technique to facilitate high quality of bank credit product in development—namely FUN—and a unified model of bank credit product quality standard for initial and further quality control. The latter is looked upon as a step forward to guarantee client-oriented focus in banking with perspective of being validated by Russian most proactive bank self-regulatory organisation—Association of Russian Banks (ARB).
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".