The social psychology of the free speech and hate speech debate
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, and university campuses. Despite the potential of examining the social psychological dynamics of this debate for advancing theory on meaning-making, polarization, emotions, and social status, empirical research in this area is scarce. This dissertation aims to address this gap by examining first-hand perspectives and media frames on the free speech and hate speech debate using digital, archival, and interview data from an online forum and four university campuses. The first empirical chapter focuses on the moral discourse of individuals within an online free speech community. I analyze 418 discussion posts on the r/FreeSpeech subreddit using a digital ethnographic approach and find that most users understand free speech in an absolutist sense but differ in their justifications for why hate speech should be allowed. The study highlights the variation in free speech discourse within online spaces. The second empirical chapter explores campus culture and students’ meaning-making processes toward speech on campus at four large public universities in the U.S. and Canada. The chapter, which draws on data from 150 student newspaper articles and 55 semi-structured interviews with students, finds the culture on each of the four campuses to be polarized around free speech issues. However, interview participants express complex and sometimes conflicting meaning-making processes, particularly around the concept of “harm,” theories about speech and how it spreads, and the roles and responsibilities of universities in society. Overall, these findings challenge the assumption that the campus free speech debate is neatly divided along ideological or moral lines. The third empirical chapter investigates how social status shapes university students’ experiences of campus speech. I draw on the same interview data and find that lower-status students express a high degree of fear and anxiety about expressing themselves openly on a range of politicized topics, including free speech itself. This self-censorship negatively impacts lower-status students’ educational experiences, sense of belonging, and professional aspirations.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.097 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".