Damage Control: How Campaign Teams Interpret and Respond to Online Incivility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social media are critical to election campaigns, but they also expose candidates to incivility and abuse. While there is a growing literature on online incivility faced by politicians, little is known about how campaign teams interpret and respond to it. To address that gap, we analyze in-depth interviews with 31 candidates and campaign staff from the 2019 federal election in Canada. We find that campaign teams interpret incivility according to the intensity of messages’ content, but also their frequency, source, and target. They use these criteria to assess potential harms in three areas: security and psychological wellbeing, strategic campaign activities, and inclusive democratic discourse. Based on these assessments, campaign teams use a limited set of platform affordances to ignore, monitor, engage, or block uncivil voices. Our analysis shows that interpretations of incivility are more nuanced and multi-dimensional than most scholarship recognizes. We also reveal the often-hidden labor that campaign teams devote to content moderation, as they try to balance protecting themselves, defending their campaign messaging, and creating space for civil discussion. By paying closer attention to campaign teams’ mediation and moderation of online incivility, scholars can better understand its consequences for democratic political participation in elections.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it