What kind of citizen? Political choices and educational goals
Bibliographic record
Abstract
The notion of democracy occupies a privileged place in our society. Educators and policymakers are increasingly pursuing a broad variety of programs that aim to promote democracy through civic education, service learning, and other pedagogies. The nature of their underlying beliefs, however, differs. “What Kind of Citizen?” calls attention to the spectrum of ideas represented in education programs about what good citizenship is and what good citizens do. Our argument derives from an analysis of both democratic theory and a two year study of educational programs in the U.S. that aim to promote democracy. The study employed a mixed-methods approach, combining qualitative data from observations and interviews with analysis of program documents and quantitative analysis of pre/post survey data. We detail three conceptions of the “good” citizen: personally responsible, participatory, and justice oriented that emerged from literature analysis and from our study. We argue that these three conceptions embody significantly different beliefs regarding the capacities and commitments citizens need in order for democracy to flourish; and they carry significantly different implications for pedagogy, curriculum, evaluation, and educational policy. We underscore the political implications of education for democracy and suggest 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.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".