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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?

2021· article· en· W3217470073 on OpenAlexaff
Monique C. Cardinal

Bibliographic record

VenueOñati Socio-legal Series · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAmnestyResistance (ecology)LawPolitical scienceAuthoritarianismPower (physics)Promotion (chess)Economic JusticeHuman rightsAdministration (probate law)Rule of lawAutonomySociologyDemocracyPolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.254
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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