“The Tears That a Civil Servant Cannot See” — Rethinking Civic Virtue in Democratic Education: A Levinasian Perspective
Bibliographic record
Abstract
The relationship between democratic life and the type of education that can best support it has been a steady topic of discussion amongst educators.According to a review article in the ERIC Digest, for example, the 1990s in particular witnessed a marked increase in the global circulation of information about the role of educational theory and practice for democracy.1 From nine of the most important "Global Trends" in "Civic Education for Democracy" that the article describes, the first and most influential involves the bringing together of the three components of civic "knowledge," "skills," and "virtues."The commonly held assumption at work here is that it is necessary to provide students not only with basic information about how governments and societies function, but also opportunities to practice behaviors like independent thinking and dialogue, while encouraging what the review calls "traits of character," such as civility and self-discipline.Illustrations of this approach are easy to find in diverse education literatures devoted to democracy and citizenship education.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".