Bibliographic record
Abstract
In this book, Giovanni Molteni Tagliabue asserts that the realization of the values and the implementation of the objectives as indicated in democratic constitutions (political, social rights and the people’s welfare) are hindered by structural defects of the legislative/government architecture and processes. Expertise and science have scarce say in politics and policy. The author suggests an innovative view through the ‘REDemo Project’. 1. Rationalization: the insertion of public scientists into legislative/executive mechanisms, with the creation in each democracy of a National Scientific Assembly – parallel to the extant partypolitical Chamber of Representatives – consisting of academic experts and researchers (legal scholars, political analysts, economists, sociologists, land/urban planners, industry/infrastructure designers, biotechnologists, agronomists, ecologists, educationists, specialists on public health, on cultural heritage, etc.) elected by universal suffrage; 2. Extension: the broadening of the institutions of direct democracy and reinforcement of the electorate as decision-maker of last resort. So the main themes and objectives of the book are an examination of the major flaws in today’s democracies (pars destruens) and a proposal for a renewed institutional framework (pars construens).
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 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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".