Bibliographic record
Abstract
Our research addressed in (e-purchasing and the legislations that must be implemented) to receive to solutions and recommendations about making e- purchasing less available, it means, that legislation must be frame accepted by these behaviors. To enhance into develop and make abundant, and prevent it from making them inhibitor in the face of its development and introducing. The importance of the study appeared in these contracts. Appear in international description in most acts upon the differences, between the ways, of concluding these e- purchasing contracts. That is, e-purchasing signed upon international web not belonged to any country, adding this web taking upon all around the world without any exclusion. What are the ways that make us receive to these aims? That we will study upon depending on the methodology of the research related to comparative analytical study between Jordanian Legislations. Make us far away from details of public descriptions, besides, legislated and theoretical discussing for most of laws and operational basis. We will manipulate in this study two chapters: the first is: objective problems and the second chapter: available data bases on the web and finally we put recommendations and results specialized in this subject.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".