Bibliographic record
Abstract
If you feel like the world has gone to hell in a handbasket, you’re not alone. If you often feel there’s nothing you can do about it, you’re also not alone. Along with this increasing anger, fear, and frustration, much confusion still prevails on the appropriate communication practices for responding to difficult situations and improving our lives. Communication experts Robert Danisch and William Keith explain why and how we can practice radical civility in this practical guide to everyday “political” communication. This guide begins with examples of radical civility to show the potential of this kind of communication to change minds and bridge differences. The authors then unpack the three foundational principles of radical civility as useful theoretical tools for thinking throughout interactions with others in civic spaces. This is then followed by a three-step process for practicing radical civility drawing on research into active listening and its importance for creating connections, validating other views, and opening up possibilities for future conversation. The guide concludes with evidence-based communication practices and prescriptive recommendations for how to do each and show examples of each in action. Radically Civil: Saving Democracy One Conversation at a Time is a much-needed communication-based antidote to polarization, preparing students, researchers, and community leaders to be responsible participants in today’s society.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".