Bibliographic record
Abstract
To counter pervasive levels of citizen disengagement from political institutions, this book examines democratic innovations that meaningfully engage with citizens to address some of the deficits of Western representative democracies. Citizens’ assemblies provide one such innovation, offering opportunities for more consistent participation between elections, more meaningful input in government decision making, and more impactful platforms for participation. This cutting-edge book introduces a new definition for an Activated Citizen, along with a methodology to measure civic and political engagement. Relying on a mixed-methods approach and field research conducted in Paris, Brussels, Ottawa, and Petaluma (California), as well as participant observations, over 180 surveys, 61 in-depth interviews and storytelling, the book provides case studies and in-depth analysis of hotbutton topics including climate change, unhoused populations, democratic expression, assisted suicide and euthanasia. Each chapter weaves quantitative results with rich qualitative testimonies from participants, government representatives, and observers. Based on empirical evidence, the book explores the ways in which government-led citizens’ assemblies can promote a more Activated Citizen. To fully realize the transformative potential of deliberative platforms, a final chapter offers a blueprint for impact, outlining concrete measures along with recommendations for the design and implementation of future government-initiated deliberative platforms. Activated Citizenship urges the deliberative community to be more discerning and intentional to more positively impact participants’ knowledge, sense of community, enthusiasm, political engagement, as well as their sense of meaningful voice. It will be required reading for all students and scholars interested in political participation and democratic innovation.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".