The Rule/Principle of No Loss at Perspective of Five Religions
Bibliographic record
Abstract
The present study examines juridical aspects of principle of no loss. The major basis of this principle is justice. Perhaps because of this reason, “principle of no loss” has priority over the rest of arguments like private and public and etc. Or it has control over generality and universality and applicability of arguments. And here, the view of jurists and lawyers is different at giving right prejudicial or give it to the injured party. This is the assumption that has been considered in most verses and hadith evidence of the principle of no loss. This indicates that “the idea of changing circumstance” before known in European rights, it had known at the rights of Islamic law centuries ago. Regarding to the views of Sunni religious leaders and scholars after them-it is clear that Sunni jurisprudence has no clear criteria for principle of no loss. Although, in many cases, Sunni religious leaders have considered the nature of the harm, but because in practice the current principle could not solve all of the issues and problems, therefore, they have resort other criteria and finally they resort to preference and correction.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".