Factors and their relationships in measuring the progress of open government
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the main factors influencing government openness, develop a global government openness index (GGOI) for assessing the progress of government openness and investigate how the factors contribute to the advancement of open government by individual countries and country groups by income. Design/methodology/approach This study identifies the four factors and adopts them into four variables for making GGOI: accountability (ACC), citizen participation and freedom (CPF), transparency (TRA) and information and communication technology (ICT). To calculate GGOI, panel data for 134 countries from 2006 to 2015 were used. Findings GGOI scores constantly improved with an annual growth rate of 2.09 percent. Countries with high ACC values tend to have high TRA scores, resulting in high GGOI scores. While the differences in ACC and TRA were steady over the period, ICT increased the most in all groups. To boost ICT performance as a channel to support other variables, middle-income countries should make further effort for citizens to use ICT capabilities toward enhancing the levels of CPF and TRA. Research limitations/implications This study presents a global picture of the advancement of open government and provides insights into specific areas that can be diagonalized. Practical implications The GGOI could be used as a useful assessment tool to measure the progress of government openness in countries and implement policies and action plans for improving government openness. Originality/value The GGOI covers the areas related to legal, administrative, participatory and technological factors and provides the factors’ inter-relationships for the composition of GGOI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".