Norm Violations in Online Discourse: Epistemic and Civil Foundations for Platform Design and Moderation
Bibliographic record
Abstract
Fostering healthy online conversations is essential to the integrity of public discourse, yet the norms that guide such conversations remain contested and difficult to enforce. This paper develops and empirically grounds a conceptual and empirical framework for understanding and addressing online toxicity. Building on the distinction between epistemic and civil norms, we argue that norm violations are the proper target of moderation. While this paper is primarily conceptual, it is informed by empirical observations drawn from a collaboration with a platform designer. Drawing on a dataset of user comments and responses to moderation scenarios, we identify eight recurring types of norm violations and analyse patterns of user agreements about whether such content should be removed. Our findings reveal that while civil norm violations prompt relatively consistent responses, epistemic violations elicit wide disagreement, raising challenges for universal moderation standards. We conclude by proposing a set of general, evidence-informed principles for platform designers and moderators. These recommendations emphasise context-sensitive moderation, platform affordances that encourage epistemic responsibility, and the integration of civil and epistemic considerations into online infrastructure. Our work bridges theoretical and practical perspectives, offering both conceptual clarity and actionable insights for scholars, designers, and practitioners engaged in shaping healthier digital discourse.
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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 | 0.094 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".