Bibliographic record
Abstract
The current and ongoing urgencies for pedagogical invention in both philosophy and practice demand teaching and learning designs and applications that have consequence in terms of civic engagement.The achievement of democratic civic understandings, values, and practices depends on the recognition by educators that they are in fact, charged with the critical and ennobling task of developing through content and action the present and future civic capacities of their students; and such capacities are antidotal to the troubling ascendency of populism, political autocracies and thuggeries around the globe.In an era irrefutably afflicted with the profusion of disinformation, the erosion of public trust, and the destabilization of truth, education for democratic participation is both intervenor and instigator for the broad project of citizenship and social change.In this sense, pedagogies are necessarily activist in their commitment to social engagement and change.The recognition of the exigencies of democratic education is more than a matter of curricula content that engages critically with the definitions and principles of deliberative democracy; rather, such recognition should result in pedagogical approaches and practices that model democratic participation especially in terms of learning environments and an infinitely more expansive view of the classroom.Key and orienting in the project of democratizing education are the determinants and elaborations of technology, and in particular AI in the educational context.While there is considerable handwringing around potential compromises to academic integrity and a rapid and unrestrained increase in academic dishonesty without efforts to neutralize the foundations and capacities of AI-generated papers, this paper explores affordances of current and emerging technologies in terms of precise practices toward democratic education, from pedagogical innovation, creative approaches to course design, new evaluation methods and criteria, expansive and experiential learning spaces, and more.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".