Bibliographic record
Abstract
During the last ten years, there has been a remarkable increase in the number of cases involving incidents of fraud in standby letters of credit. The purpose of this thesis is to examine the effect of fraud on standby letters of credit transactions. This study principally deals with standby letters of credit issued under the 1983 Revision of the Uniform Customs and Practice for Documentary Credits, drafted by the International Chamber of Commerce, which is a set of internationally recognized rules for documentary credit operations. Due to the international character of letters of credit law, I have adopted a comparative approach that deals with materials from the U.S., Canada, U.K., Germany and Switzerland. This thesis will first show that there are two types of standby letters of credit, which have to be distinguished because they involve different obligations and risks for the parties. A device payable against the beneficiary's simple statement will be described as a "simple statement" standby credit, whereas a device payable against a set of documents will be called a "documentary" standby credit. The thesis will then demonstrate why the treatment of fraud should not be the same for "simple statement" standby credits and "documentary" standby credits.
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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".