Towards an Epistemic Compass for Online Content Moderation
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
Abstract The internet provides easy access to a wealth of information that can sometimes be false and harmful. This is most apparent on social media platforms. To combat this, platforms have implemented various methods of content moderation to flag or block content that is inaccurate or violates community standards. This approach has limitations – from the epistemic injustices that might occur due to content moderation practices to the concerns about the legitimacy of these for-profit platforms’ epistemic authority. In this paper, I highlight some of the epistemic challenges of online content moderation with a focus on how it harms internet users and moderators. If we are to moderate content effectively and ethically, we must attend to these challenges. Hence, I map out an epistemic compass for online content moderation that looks to attend to these challenges. I argue for a pluralistic model of content moderation that categorises content online and distributes the task of content moderation between human moderators, automated moderators, and community moderators in a way that plays to the strengths of each content moderation model. My compass is beneficial for two reasons: first, it allows room for the internet to realise its potential as a democratising force for knowledge, and second, it helps minimise the epistemic downsides of relying on profit-driven companies as epistemic authorities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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