Re-engaging Citizens in Europe and North America
Bibliographic record
Abstract
Abstract The spread of PB in the North Atlantic region (Europe, the United States, and Canada) is taking place as citizen apathy, declining trust, social exclusion, and growing inequalities spread in these wealthier democracies. By 2016, major cities such as New York City, Paris, Madrid, Barcelona, Lisbon, Chicago, Boston, Seattle, Toronto, and Seville adopted some form of PB. The national governments in Poland and Portugal now mandate some form of PB. The authors see significant institutional innovation in these PB processes as PB’s original rules have been reimagined to address different types of problems. New York City and Chicago initiated their PB programs at sub-municipal levels. Paris, Madrid, and Barcelona had adapted their PB programs to strongly emphasize online participation. At the broadest level, PB in Europe and North America is more heavily geared toward civic education and community empowerment than toward the redistribution of spending priorities. In some place PB remains a democratic institution that retains some of the radical features of the first wave but it is also a policymaking tool in other places, designed to generate government efficiencies. Most importantly, most programs retain the radical idea that a wide variety of citizens, especially those from politically weaker and more marginalized groups, should be directly involved in decision-making.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".