Comparative Study of Hire-purchase in Iran and English Common Law
Bibliographic record
Abstract
Hire-purchase is a mutual interest contract. Regarding its commutative nature, exchangeable items that are against each other are exchanged in contracting process, so if contract is null or is canceled for any reason according to the contract terms, in the way that transferring possession is impossible, relevant actions will be based on the contract if funds entitled monthly installments are determined in. Therefore, the current paper aims to comparatively study hire-purchase in Iranian and common law. Analytical-descriptive method is applied in the paper. The findings indicate the difference between hire-purchase in Iran and common law is that contract for common law is just utilized for movable properties; while in Iran law it is utilized for both movable and immovable properties. In England law, hire purchase is a specified contract. According to the England hire purchase law and consumer credit law in 1974, hire purchase is a contract in which leased goods are transferred from creditor to the credit receiver instead of using periodic payment. It happens only when the credit receiver fulfills the contract terms. In other words the hire purchase contract used in England law is a hire contract with tenants’ rights of possession, while it has not been explained in the Iranian law.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".