Bibliographic record
Abstract
Misrepresentation is such as controversial issues of contract law.In Iranian law, misrepresentation is defined as "deceiving the main motivation for signing contracts".In Iranian law, misrepresentation will not constitute the defect, Because the defect of determination (consent) is unique to the reluctant and confused.In jurisprudence, the misrepresentations of fact on the contrary party is deemed to be misrepresentation.And the misrepresentation (option termination) is considered only way and the most effective way for the injured person according to enforcement since Iranian law is based on Islamic law .Therefore, the Iranian Civil Code (option termination) is the most effective way to prevent a loss.The purpose of this paper is to examine the meaning of the provisions of this act of misrepresentation and jurisprudence in law of Iran.In all the world, traders to attract others in transaction or decorate selves production or by using actors and women propaganda it in special conditions, an exaggeration praise of their production or their commodity.Misrepresentation is not always bad and unacceptable, sometimes misrepresentation occurs to the favorites extent permitted of parties which called to authorized misrepresentation; however, public opinion has always shown a negative reaction to the word misrepresentation and it is deemed obscene.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.954 | 0.936 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".