Bibliographic record
Abstract
The development of financial markets and the low level of financial literacy does not facilitate consumer protection. A significant problem is the lack of information or unclear information regarding financial offers, including consumer credit. Financial protection for consumers can be increased by using systems that support consumers faced with a lack of transparency of consumer credit offers. The theoretical objective of the research is to identify the completeness of information allowing for verifying the annual percentage rate (APR) in the consumer credit offers presented and compared on websites of financial intermediaries and banks as well as the identify of APR calculation methods. After analysis of different credit offers (document analysis), a prototype of the system regarding credit costs measured at the appropriate interest rates (APR and AER) calculated in the correct manner was created. This may facilitate practices for informing consumers about the characteristics of consumer credit and at the same time support the implementation of the concept of responsible lending. The system developed uses a computer to simulate human thinking and to augment it with artificial intelligence. It facilitates the elimination the behavioral biases during the taking of financial decisions, which are the result of a low level of financial literacy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".