Bibliographic record
Abstract
Background/Context: Social scientists, policymakers, and commentators have long assumed that Western democracies enjoy relative stability because of deep commitments to a culture of democratic governance. But those commitments are quickly fading in almost every developed and developing democracy around the globe. In the same period in which support for democracy has declined, schools and teacher education programs have been pressured by "accountability" measures and economic austerity to focus on math and literacy achievement to the exclusion of nearly every other educational goal. These challenges to social cohesion and democratic governance highlight the need for young people to be exposed early on and throughout their educational pathways to the knowledge, skills, and dispositions consistent with democratic life. The narrowing of curricular goals, therefore, is a threat to the stability of democratic institutions. Purpose: This essay examines the potential of teacher education as a lever for change. How might teacher educators ensure that teachers are prepared to foster education that will sustain and strengthen democratic norms? If schools have an essential role to play in preparing students for informed engagement in civic and political life, how can we best prepare teachers to advance those goals? Research Design: This is an analytical essay drawing on recent empirical research on declining support for democratic values and on teachers' civic engagement as well as conceptual work on democratic education goals. To illustrate the potential for teacher education to prepare teachers to engage students in political issues discussions, I draw on data from the first large-scale empirical study of what U.S. high school teachers currently do to prepare youth to understand economic inequality and its causes, effects, and possible remedies. The study included a teacher survey and follow-up interviews concerning teachers' political ideology and civic and political engagement as well as classroom practice. The 2,750 teachers who participated in the survey are representative of U.S. public schools (and an additional segment of U.S. elite independent schools) in terms of student demographics and geographic location. We also conducted 150 follow-up interviews. Conclusions/Recommendations: I suggest that teacher education must move from reactive technocratic concerns for accountability and standardization to broader civic and civil commitments to the foundations of democratic community, pluralism, and relationship. Teacher education programs should consider ways to encourage new and experienced teachers to follow the news, engage in civil discourse with one another about topics of public concern, and participate in civic and political life. Moreover, teacher educators could work toward teaching the knowledge, skills, and dispositions associated with political and civic engagement.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".