The Moral Discourse of Free Speech: A Virtual Ethnographic Study
Bibliographic record
Abstract
Freedom of speech has long been considered an essential value in democracies. However, its boundaries concerning hate speech continue to be contested across many social and political spheres, including governments, social media websites, and university campuses. Despite the recent growth of so-called free speech communities online and offline, little empirical research has examined how individuals embedded in these communities make moral sense of free speech and its limits. Examining these perspectives is important for understanding the growing involvement and polarization around this issue. Using a digital ethnographic approach, I address this gap by analyzing discussions in a rapidly growing online forum dedicated to free speech (r/FreeSpeech subreddit). I find that most users on the forum understand free speech in an absolutist sense (i.e., it should be free from legal, institutional, material, and even social censorship or consequences), but that users differ in their arguments and justifications concerning hate speech. Some downplay the harms of hate speech, while others acknowledge its harms but either focus on its epistemic subjectivity or on the moral threats of censorship and authoritarianism. Further, the forum appears to have become more polarized and right-wing-dominated over time, rife with ideological tensions between members and between moderators and members. Overall, this study highlights the variation in free speech discourse within online spaces and calls for further research on free speech that focuses on first-hand perspectives.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 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".