Bibliographic record
Abstract
This scoping review examines individual and societal use cases of Bitcoin in the peer-reviewed literature. Arksey and O’Malley’s scoping review methodology was used, and a comprehensive search strategy was employed using Web of Science and Engineering village databases. Articles were screened at the title and abstract and full-text levels by the authors. One author conducted data extraction to summarize the data. In total, 17 relevant articles were included in this review. Investment and savings were the most widely reported use cases at an individual level, with payments and international transfers less frequently reported in the studies. Only two studies reported on societal use cases of legal tender; however, only one country, El Salvador, executed its intention. Our study suggests that Bitcoin is being used by individuals around the world with little report of societal (e.g., country adoption) uses cases. For example, there is evidence on the internet and on a grass-roots level that Bitcoin is being used in circular economies; however, the peer-reviewed literature may not yet capture the extent and full benefits and challenges. As such, we provide ideas for future research to more comprehensively explore Bitcoin uses and its impacts on individuals and society.
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 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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".