Challenges of Paperless Trade: Redesign of the Foreign Trade Processes and Bundling Functions of Traditional Documents
Bibliographic record
Abstract
Technological innovations in recent years have resulted in paper based documentation will be completely abandoned in all business processes, however the paper-based processes still prevail in foreign trade transactions due to complicated business processes. Institution and companies in different countries involved in foreign trade transactions. Therefore integrative solutions are needed. This paper seeks to put forward a methodology to pave the way to the future international trade. Therefore we developed a taxonomical approach to this clutter in order to partially surmount the integration problem because a road map should be determined before the holistic solution. Taxonomical approach has been suggested for grouping the foreign trade documents in terms of their functions. All the parties participating in a foreign trade transaction should be able to connect to each other in a single online platform. There is a need for a single online platform of which all parties are members to complete a foreign trade transaction using a single integrated electronic document. Finally, opinions of the professional have been received for seeing the real procedures. Literature and practices have been synthesized and partial solution grouping the foreign trade documents in terms of their functions has been suggested. To constitute a base for the initial step of the road to integrated foreign trade document a taxonomical table has been prepared. Aim of this table to guide the firms that search for solutions to develop electronic equivalent of paper based documents.
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".