Civic Engagement and Civic Attitudes in Cross-National Perspective: Introduction to the Symposium
Bibliographic record
Abstract
Although civic engagement as a field of study has a long tradition in political science, it re-emerged in the 1990s as a result of real world events and academic scholarship. Popular revolutions in Eastern Europe and elsewhere led to a renewed interest in ‘people power’ across the world, including in the United States.And political scientist Robert Putnam’s study of civic community in Italy, followed by his analysis of changing patterns of social capital in the United States, helped to launch a new research agenda in the fields of comparative and American politics. Indeed, over the last fifteen years since the publication of Making Democracy Work, and over a decade since the article versions of what eventually became Bowling Alone, numerous works have addressed the themes of civil society and social capital, in a wide variety of theoretical and empirical contexts (see, for example, Adler and Kwon, 2002; van Deth, 1997; Foley and Edwards, 1999; Glaeser et al., 2002; Hooghe and Stolle, 2003; Howard, 2003; Lidstrom, 2006; Lin, 2001; Newton, 2006; Paxton, 2002; Portes, 1998; Woolcock, 1998).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".