Innovation, Ethics, and Consumer Protection
Bibliographic record
Abstract
In recent years, firms that rely on fintech to develop and deliver financial products and services have become increasingly important in the banking and financial sector. Nevertheless, fintech products raise various ethical and legal issues, particularly relating to consumer rights. These issues can be analyzed in the light of behavioral law and economics which suggest that prevalent cognitive biases systematically affect the judgment of individuals. To mitigate this bounded rationality, it is possible to guide the decision-making processes through the use of nudges and configured sets of choices. A good example is fintech gamification, defined as the use of game-related mechanisms in other areas, particularly websites or social networks. However, the use of nudges for commercial purposes is often associated with some form of manipulative tactics explicitly aimed at exploiting individuals' biases without their consent. In this context, recourse to the law is useful as it provides for the minimum requirements needed to regulate the relationship between a firm and its customers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".