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Record W2909979169 · doi:10.1080/13510347.2019.1566903

Beyond elections: perceptions of democracy in four Arab countries

2019· article· en· W2909979169 on OpenAlexaff
Andrea Teti, Pamela Abbott, Francesco Cavatorta

Bibliographic record

VenueDemocratization · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDemocracyPoliticsScholarshipPrima facieLiberal democracyPolitical economyPublic opinionGovernment (linguistics)Political scienceValue (mathematics)SociologyCivil societyLawRepresentative democracyWorld Values SurveyPublic administrationLaw and economics

Abstract

fetched live from OpenAlex

This article draws on public opinion survey data from Morocco, Tunisia, Egypt, and Jordan to investigate first, whether a “demand for democracy” in the region exists; second, how to measure it; and third, how respondents understand it. The picture emerging from this analysis is complex, eluding the simple dichotomy between prima facie support and second order incongruence with democracy, which characterises current debates. Respondents have a more holistic understanding of democracy than is found in current scholarship or indeed pursued by Western or regional policymakers, valuing civil-political rights but prioritizing socio-economic rights. There is broad consensus behind principles of gender equality, but indirect questions reveal the continuing influence of conservative and patriarchal attitudes. Respondents value religion, but do not trust religious leaders or want them to meddle in elections or government. Moreover, while there is broad support for conventionally-understood pillars of liberal democracy (free elections, a parliamentary system), there is also a significant gap between those who support democracy as the best political system in principle and those who also believe it is actually suitable for their country.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.296
Teacher spread0.287 · 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 designObservational
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

Citations67
Published2019
Admission routes1
Has abstractyes

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