Comparing Subject of Assignment of Contract with Similar Concepts of Other Countries' Domestic Laws and International Documents
Bibliographic record
Abstract
In concluding a contract, the thing being concluded and assigned is a contract which is a credit existence considered as object of assignment. However in assigning liabilities and debts, the thing being assigned is a debt, which is called debt for the debtor and right for the creditor, whether this liability or debt is due to a contract or due to a crime or civil liability and tortious liability. In novation, what is important is fall of the previous obligation and establishment of the new one. Hence in novation you cannot only rely on assigning obligation with the previous status, since the previous obligation does not remain anymore. This is while making an contract has this advantage that without any necessity to fall of the previous obligation, position of the obligor and obligee can be replaced. Each contract, from viewpoint of each of the parties to the contract, bears two parts including rights and obligations. According to the aforementioned issues, rights and obligations can be assigned separately. Now, when one party to the contract assigns both rights and obligations caused by a contract in a legal action, actually an assignment of contract has occurred. In other words, assignment of contract is total assignment of rights and assignment of obligations. Therefore, in comparing these subjects, assignment of contract is general and the other two are specific.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".