An Economic Analysis of Iran’s 2017 Judicial System Reforms: Ways with Long-term Effects to Improve Judicial System’s Litigation Delay
Bibliographic record
Abstract
Litigation delay is a serious concern for judicial systems. In 2017, Iran enacted regulations for digitizing the judicial system in order to address this problem. This article shows whether this new policy has been an efficient move and shows solutions with more long-term results for overcoming the litigation delay. To analyze the recent reforms' efficiency, I review Iran's dispute resolution performance using secondary data from Doing Business research and the Research Center of the Iranian parliament reports in measuring the doing business environmentfrom 2016, before adopting those regulations, and then until 2019. Finally, it is concluded that Law & Economics methodology is a suitable methodology for analyzing the efficiency of 2017 Iran’s policy, which also provides ways to achieve more sustainable results to overcome the litigation delay. The main finding of this study is that according to the Kaldor-Hicks efficiency, Iran's recent reforms related to digitizing the judicial system have been an efficient move; however, due to the nature of these reforms, this efficiency does not last permanently.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".