An Analysis of the Role of Human Dignity in the Iranian Citizens Rights Charter
Bibliographic record
Abstract
In today's societies, in which the variety of social communications are increasingly expanded, citizenship rights in relation to all citizens equally and without discrimination depends on a comprehensive charter. This charter should specifically predict citizenship rights. The citizenship Bill of Rights will only be successful in achieving its goals in case it is principally based on the human dignity. The Iranian legal system in 1392 experienced the development of the "Citizens Rights Charter". This charter, with its fundamental drawbacks, will not have a desirable impact on the Iranian legal system. Apparently, human dignity enjoys a proper position in the introduction and the general rules of the Iranian Citizens Rights Charter. However, the charter's understanding of the concept of citizen and government has compromised this condition. On the one hand, considering the citizen as anonymous with the national, and granting citizenship right to the state on the other hand have compromised the the position of human dignity in the charter. With respect to the instances of civil rights, human dignity does not enjoy an appropriate position too. The lack of distinction between instances of human rights and mere citizenship rights, non-implementation of instances in a comprehensive framework and the over-emphasis on counting the instances by the law, has undermined human dignity in the citizen rights context.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".