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 platforms face increasing pressure to moderate harmful content while preserving user engagement and free expression. We examine shadowbanning—a strategy that hides content without notifying the user—and compare it to traditional content removal. Our results show that if users only moderately believe that shadowbanning occurs, the platform benefits from a larger user base and higher profit, which also leads to greater social welfare than with content removal or no moderation. Shadowbanning allows the platform to reduce users’ exposure to extreme content without deterring content creators, enabling more participation of users across the extremeness spectrum. However, outcomes depend on user beliefs and the accuracy of moderation technology. When users are highly suspicious of shadowbanning or when moderation tools are significantly imperfect, the platform’s incentives—and the societal benefits—decline. These findings offer practical insights for platform designers and regulators: shadowbanning can be effective, but its benefits hinge on how transparently and accurately it is implemented. Policymakers should account for user perceptions and technological capabilities when evaluating or regulating opaque moderation strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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