Direct democracy and AI as a way to revitalize the health of the Federal Commonwealth
Bibliographic record
Abstract
Democracy stands as the cornerstone of our modern world and current achievements; however, its present foundation was laid mainly in the 18th century, a time marked by slavery, widespread conflicts, imperialism, significant illiteracy, lack of advanced technologies, etc. While the global landscape has evolved, democratic institutions have not progressed simultaneously. Integrating artificial intelligence into our lives, alongside the practical implementation of direct democracy, provides a glimpse of potential enhancements that might propel us to a new level of governance—a vision articulated by A. Toffler and other thinkers. These enhancements could significantly boost societal knowledge, bringing us closer to the emergence of a knowledge society both chronologically and conceptually. On the other hand, AI systems pose risks to democracy, including limiting our free will and creating digital slavery. The trajectory of our progress depends on the decisions we make today. These issues are the focus of the paper’s comprehensive and pragmatic analysis.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".