Current Laws Governing Franchise Agreement in Jordan
Bibliographic record
Abstract
Currently there are more than 150 local and international franchise businesses operating in Jordan. Franchise business in Jordan has been a crucial investment market contributing to the country’s Gross Domestic Product (GDP) and developing its economic growth and trade. Nevertheless, legal challenges to the investors which have existed may hinder them from opening up a franchise business in Jordan. One of these challenges is the lack of specific legal framework regulating franchise business. Jordanian legal system does not have specific legislation to regulate the franchise agreement (which is known as the “license agreement” in Jordan) between a franchisor and a franchisee. The lack of specific legislation may deter or at least slow down the progress of foreign and local investors in setting up franchise businesses in Jordan, as they could not reasonably anticipate the relevant laws and regulatory enforcements relating to franchise. Therefore, this paper examines the current laws and regulations governing franchise business in Jordan. The paper concludes that existing laws affecting franchise in Jordan fail to address comprehensively the legal aspects of franchise. Thus, there is a dire need for specific legal framework to govern franchise business in Jordan.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".