Democratic pedagogies: What are they and why do we need them?
Bibliographic record
Abstract
In contexts of escalating political repression, democratic backsliding, and structural inequality, this special issue explores how democratic pedagogies can serve as tools for resistance, imagination, co-creation and transformation. Grounded in love, joy, and justice, the contributions challenge dominant educational norms that commodify knowledge and silence dissent, advancing instead pedagogies rooted in relationality, reciprocity, decoloniality, and self-governance. Contributors theorize and practice education as a collective, political act, one that navigates and contests hierarchies of race, gender, class, and colonial power, while fostering inclusive, co-created learning spaces within and beyond the “classroom”. The issue is organized around three thematic pillars: democratic pedagogies as praxis; community-engaged reimaginings of the “classroom”; and experimental, participatory designs for democratic learning. Together, these works illuminate how pedagogy can enable self-governance, center marginalized knowledges, and cultivate collective agency. Emphasizing intersectionality, plurality, and creativity, this issue asks what it means to democratize education under conditions of uncertainty and how pedagogical spaces can be reclaimed as sites of world-making. Through diverse methodologies and grounded practices, it contributes to an urgent dialogue on how democratic pedagogies can confront authoritarianism and fascism, while building more just, inclusive, and transformative educational futures. We conclude the introduction by offering recommendations for how you can co-design, foster collaboration, and enact democratic pedagogies in your own contexts.
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 | 0.046 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.041 | 0.056 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".