What Teachers Believe about Democracy and Why it is Important— How (Should) We Prepare Students for Democracy and Citizenship
Bibliographic record
Abstract
Democracy means many things to many people. There is much discussion that democracy is in now in decline or even in crisis citing apparent youth apathy and disengagement. The research which this chapter reports on seeks a more robust, critical, or thicker interpretation of what democracy is; what it should be; and, significantly, how it can be beneficial to all peoples. The traditional approach in civics and citizenship education focuses on understanding political structures, often isolated to a single unit of study on and teaches about democracy not necessarily for democracy. This chapter argues that a broader, more participatory, critical, and relevant educational experience that includes a critical use of social media and critical digital literacy is fundamental to facilitating a process of meaningful societal transformation through thick democracy. Three questions related to democracy are framed within the context of empirical and qualitative data gathered from a study of over 600 teachers. This chapter uses a critical framework to elucidate the potential for transformation of our individual and collective sense of democracy and builds on previous studies in the U.S. and Canada together with research being conducted in over 25 countries with educators internationally through the Global Doing Democracy Research Project.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".