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Record W7128473279 · doi:10.64903/1480-6800-28.1.21

Statistical Analysis of Perceptions of Jordanian Women's Access to Parliament from the Perspective of Female Members of Jordanian Political Parties

2025· article· W7128473279 on OpenAlexvenueno aff
Raafat A. Tarawneh, Areej Ali Khalil Jaber

Bibliographic record

VenueArab world geographer · 2025
Typearticle
Language
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentPoliticsLegislationDemocratizationGovernment (linguistics)PrismStatistical analysis

Abstract

fetched live from OpenAlex

This study seeks to shed light on a widely discussed contemporary social issue in Jordan, which has presented itself repeatedly in each round of elections to the Jordanian Parliament in the period 1989-2020. Although the Jordanian women's right to participate in the fabric of political life of the state is well-established and enshrined in legislation and government backing, support for their election to Parliament among the public remains consistently low. This study is an attempt to explain the persistent paradox between a low rate of female candidate access to Parliament beyond their legally assigned quota and their levels of achievement in all spheres of progress, democratization and development in Jordan. In doing so, the authors investigate their chosen research topic through a detailed statistical prism and analysis of questionnaires distributed among 270 female respondents affiliated with political parties. Such analysis revolves around two core problematic parameters: modernizing the political system in Jordan and the role of social support.

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)0.009
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.332
Teacher spread0.314 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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