HATRED OF/AND DEMOCRACY: THE POLITICAL CONTRADICTIONS OF REDDIT’S MODERATION STRUCTURE
Bibliographic record
Abstract
This paper seeks to interpret Reddit moderation as a problem of political theory, rather than as a debate between the merits of human moderation and algorithmic moderation. Analyzing Reddit’s moderation structure shows that both the human moderation and the algorithmic moderation reinforce a form of anti-politics which leaves users feeling like they have no input and thus no interest in the well-being of the subreddits in which they participate. Online governance structures are largely top down and authoritarian in nature, despite often being couched in democratic rhetoric, reflecting what Jacques Rancière describes as a hatred of democracy. By looking at the example of how r/Canada came to be widely disparaged on Reddit as a bastion of hate, I make the argument that the key to rooting out online hate is not through more human moderation or by giving algorithms more control, but by creating a democratic culture of buy-in through which users are empowered with responsibility for the quality of content in a discussion space.
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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 | 0.027 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".