Review of American Notary System - New Developments, Challenges and Its Coping Strategy
Bibliographic record
Abstract
Back in 2010, President Barrack Obama vetoed a bill -- Interstate Recognition of Notarizations Act (know as H.R.3808) - that requires courts and other entities to recognize licensed notaries. The notaries, from all states, create a lot of attention towards a topic rarely discussed in the public domain. Despite the fact that most individuals view the notarization process as a formality, it has significance on the states governance. State government officials in charge of overseeing the notary commission realize the gravity and significance of this function. Notaries have a variety of rules in various governments. They establish the bonafide of signatures for protecting transactions from forgery and fraud cases. The presence of a notary’s signature bolsters a document’s authenticity. The notary system, however, faces a variety of challenges in meeting its obligations. Issues rise in the coordination of transaction security and contract freedom, transaction security and efficiency, and the incorporation of information and communications technology (ICT) and internet into the notary system. The research paper explores the problems in high detail. It focuses on the state of the notary system, previous research, challenges, effects and means of improving the efficiency of the system, through adjustments.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".