Bibliographic record
Abstract
As a result of monumental improvements in technology, a significant amount of currency is spent across the globe in daily transactions made on the internet. A recent trend in American culture is brick and mortar businesses shutting down in favor of online counterparts.1 Some of the many possible reasons behind this online movement may be a decrease in overhead, a convenience factor for consumers, and a drastically expanded market of consumers. In the third quarter of 2015, Americans spent an estimated $87.5 billion dollars on online shopping.2 These e-commerce transactions comprise an impressive 7.4% of total retail sales made in the United States.3 This figure has increased dramatically from the 2.6% of total retail sales made in the first quarter of 2006 and continues to steadily rise.4 Aside from the major financial implication from online purchases made in America, the global market for e-commerce is astronomical. Since it is next to impossible to pay on the internet with cash, bank-issued credit cards are the predominate method of payment. With credit cards, currency can be exchanged on the internet in the blink of an eye. However, there are several drawbacks associated with the global use of these credit cards including fees imposed by major credit card companies and the high risk of credit card fraud. With a continually growing global economy that is largely fueled by internet transactions, the world could benefit tremendously from a safe and inexpensive globally accepted method of payment
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".