Public and Private Ownership in the Iranian Constitution: Principle 44, A Barrier for Development
Bibliographic record
Abstract
The Iranian legislator has sought to protect public property and public ownership in the Iranian Constitution in accordance with Islamic principles, terms and procedures. There are a number of principles that have been directly applied to this purpose; however, one principle has had a very significant impact on government domination of the economy of Iran: principle 44. This principle does not directly describe public property; rather, it aims to determine the areas that are under public ownership and are administered by the government. However, the principle has some contradictions and legal challenges in itself. In addition, the supplementary law that has been enacted to provide the areas for the enforcement of principle 44 fails to secure the aims of the legislator. Therefore, this paper analyses legal challenges of the principle, as well as its supplementary law, and gives suggestions to solve the challenges.This paper is divided into four sections. The first section provides an analysis of the principle itself, and its relationship and consistency with other principles of the constitution. In the second section, the Law of Implementation of Principle 44 and the legal challenges that arise from it will be discussed. The third section focuses on the negative economic impacts of this law, as well as case studies of it. Lastly, the paper provides a summary of suggestions to amend this law.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".