MétaCan
Menu
Back to cohort
Record W2756097191 · doi:10.1177/0266666917731946

Developing a Government Openness Index: The case of developing countries

2017· article· en· W2756097191 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInformation Development · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsMcGill University
FundersHankuk University of Foreign Studies
KeywordsOpenness to experienceInformation and Communications TechnologyDeveloping countryTransparency (behavior)AccountabilityIndex (typography)Government (linguistics)Panel dataEconomic freedomOpen governmentBusinessPublic economicsFreedom of the pressEconomic growthPolitical scienceEconomicsEconometricsComputer sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

This study aims to develop a comprehensive Government Openness Index (GOI) in developing countries, explore the relationship of the variables in the GOI, and examine the relationship of the GOI and income levels. Based on a linear scaling method, panel data from 101 countries was used to develop a GOI using four variables (e.g. accountability (ACC), information and communication technology (ICT), citizen participation and freedom (CPF), and transparency (TRA)). The results show that ICT performs highest in global means, coefficients of variation, and the contribution rate and contribution level to the change of GOI but CPT performs lowest in the contribution rate. The relationship between GOI and income is significantly positive. The results of this study suggest that developing countries should improve their capacity to utilize and sustain ICT and in particular human capacity for directing ICT toward improving citizen participation and freedom.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.033
GPT teacher head0.307
Teacher spread0.274 · 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