Using Panel Data Analysis to Uncover Drivers of E-Participation Progress
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.
Bibliographic record
Abstract
This paper examines and uncovers the key drivers of e-participation progress or growth over the years, globally and regionally. The authors used fixed-effects regression model on a panel data of variables gathered by reputable world organizations for an 8-year period – one of the largest examined to date. They tested a research model including GDP per capita, ICT infrastructure, secondary education enrolment, technological knowledge creation and outputs, and six governance indicators: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. At the global level, the results indicate that e-participation progress is positively influenced by voice and accountability, GDP per capita, and ICT infrastructure. Analyses based upon six geographical regions of the world and countries' income-level classifications (i.e., low, low-middle, high-middle, high) show that determinants of e-participation progress vary by geographical and income-level contexts.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it