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Record W4284892399 · doi:10.1163/15730255-bja10120

Thickening Autocracy in a Non-Democratic State: Changing Demographics in Syria to Maintain Authority

2022· article· en· W4284892399 on OpenAlexaff
Ghuna Bdiwi

Bibliographic record

VenueArab Law Quarterly · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsWilfrid Laurier UniversityUniversity of Ottawa
Fundersnot available
KeywordsAutocracyAuthoritarianismPolitical economyLegitimacyPopulationPoliticsGovernment (linguistics)IndigenousState (computer science)LawPolitical scienceDemocracyPower (physics)SociologyDevelopment economicsEconomics

Abstract

fetched live from OpenAlex

Abstract This article analyses several authoritarian practices in Syria since 1971 and demonstrates that, since the 2011 uprising, its authoritarian regime has successfully remained resilient instead of collapsing. The post-2011 Syrian Government under Al-Assad is no longer the Ba`thist government of old, albeit still autocratic but adept at adapting to hostile changing political environments. Al-Assad’s regime no longer relies on Ba’ath Party loyalty and appearances of legitimacy but both during and post-war has depended more on social re-engineering to sustain its political, economic power. The Syria example demonstrates that, when threatened, authoritarian regimes may thicken the layers of their autocratic rule to sustain their grip on power, even changing the composition of its citizenry to create a new population to rule. We demonstrate how the Syrian Government has used urban planning, housing, and property laws to re-engineer its demographics so that friendly foreign nationals will receive permanent citizenship and displace indigenous citizens.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.013
GPT teacher head0.288
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2022
Admission routes1
Has abstractyes

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