THE JUDICIAL MECHANISM IN RESOLVING AIR CONSUMER DISPUTES: A CRITICAL ANALYSIS OF CHALLENGES AND SOLUTIONS IN THE IRAQI SYSTEM
Bibliographic record
Abstract
This study addresses the judicial mechanisms available for resolving consumer disputes in the field of air transport in Iraq, through a critical analysis of the challenges hindering the protection of passenger rights when disputes arise with airlines. Given the rapid developments in the global air transport sector, the judiciary becomes a vital tool for ensuring justice and protecting consumer rights. However, the Iraqi judicial system faces several challenges that hinder the swift and fair achievement of justice in this field. The main challenges consumers face in air transport include high litigation costs, the complexity of legal procedures, long waiting periods in courts, and the difficulty of proving fault against airlines, especially due to the lack of complex technical evidence. Furthermore, the lack of legal awareness among passengers about their rights makes it more difficult for them to claim compensation or defend their rights in court. These problems have been exacerbated by Iraq's failure to adopt the 1999 Montreal Convention, which strengthens consumer protection in the field of international air transport. The study aims to assess these mechanisms and offer solutions to improve the efficiency of the Iraqi judicial system in line with international standards. The study is based on a review of the relevant legal provisions.
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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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".