Bibliographic record
Abstract
FinTech has often been spoken about in highly promising terms, deemed to have a profound and potentially revolutionary effect. This has led to speculation and intrigue about whether this innovative form of technology might have the capability to influence an entire economy significantly. More than that, some even contend that it carries the potential to alter societies dramatically through its direct impact on both existing economic frameworks and social structures. Thus, a vital question has risen to the forefront: could FinTech indeed be seen as a general-purpose technology? Following the initial inquiry, a second question emerges, delving deeper into the overall impact of FinTech. The focus is on understanding how it influences things at an aggregate level and as a potential general-purpose technology. How does it affect aggregate economic welfare? The paper conducts an in-depth analysis using two distinctly different definitions and characteristics of general-purpose technologies. By leveraging these definitions, the document provides valuable insights into how FinTech aligns with the attributes of a general-purpose technology, effectively showcasing that it can indeed be typified as such. Despite the growing body of research on FinTech, no study thus far has examined the implications or influence it has on welfare. At an aggregate level, the research findings indicate that FinTech influences supply curves positively. In turn, this results in a noticeable uptick in both consumer and producer surplus, bolstering overall welfare. The examination thus reveals how FinTech is indeed a reckoning force in modern economics, and potentially a game-changer. Thus, its significance as a general-purpose technology and the value it brings to aggregate economic welfare cannot be underestimated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".