Educators as Civic Agents: Promoting Civic Literacy and Teaching About Controversial Topics in Schools
Bibliographic record
Abstract
This paper examines the essential role of teachers as civic agents in promoting civic literacy and teaching controversial topics. Amid restrictive policies, curriculum limits, and societal norms that often exclude marginalized perspectives from the classroom, the study asks: How can teachers promote civic literacy when crucial perspectives are excluded from school curricula? Combining Dewey’s theory of democratic education with recent studies on civic literacy, this study situates classrooms as a foundational environment necessary for cultivating critical thinking, civic identity, and a commitment to equity, justice, and the common good. An autoethnographic methodology, which blends personal anecdotes with literature, helps demonstrate practical strategies such as dialogic teaching, storytelling, and experiential learning, which enable students to navigate sensitive topics and develop as active, involved citizens. The findings demonstrate that intentionally engaging with, rather than avoiding, so-called controversial topics fosters inclusive democratic participation and challenges the notion that education is inherently neutral. This contributes an original argument: civic literacy is inseparable from openly discussing real-world inequities and supporting teacher agency. Recommendations for curriculum reform and professional development to support teacher agency in navigating these issues suggest that schools are essential to the renewal of democracy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".