What Kind of Citizen? The Politics of Educating for Democracy
Bibliographic record
Abstract
Educators and policymakers increasingly pursue programs that aim to strengthen democracy through civic education, service learning, and other pedagogies. Their underlying beliefs, however, differ. This article calls attention to the spectrum of ideas about what good citizenship is and what good citizens do that are embodied in democratic education programs. It offers analyses of a 2-year study of educational programs in the United States that aimed to promote democracy. Drawing on democratic theory and on findings from their study, the authors detail three conceptions of the “good” citizen—personally responsible, participatory, and justice oriented —that underscore political implications of education for democracy. The article demonstrates that the narrow and often ideologically conservative conception of citizenship embedded in many current efforts at teaching for democracy reflects not arbitrary choices but, rather, political choices with political consequences.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".