The disempowerment of the judiciary in Syria since the March revolution of 2011 and the emergence of off-bench resistance to authoritarian rule: What role for women judges and prosecutors?
Bibliographic record
Abstract
The Arab uprisings of 2010-2011 generated a growing movement for change among the judicial corps throughout the Arab world. Judges and prosecutors created independent associations in Morocco, Mauritania, Yemen, Libya, Lebanon, and Tunisia to represent their interests and promote a better administration of justice. Since the March Revolution of 2011 in Syria, members of the judiciary also attempted to create their own association, but failed to do so. This article briefly outlines the demographics of the judicial corps after ten years of conflict in Syria. A noticeable change is the increase in the number of women in the judiciary and their promotion to positions of power. How have women judges and prosecutors used the greater authority granted to them? To the advantage of the regime, as a means for self-promotion or to better defend the rights of all? The second part of the article details the progressive disempowerment of the judiciary, the expansion of the criminal justice system and the creation of the Counterterrorism Court used by the regime to quash the popular uprising. In the final section, stories of off-bench resistance highlight efforts made by judges and prosecutors to defend their judicial autonomy and the basic human rights and freedoms of all Syrians.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".