Bibliographic record
Abstract
It is frequently claimed that online disinformation threatens democracy, and that disinformation is more prevalent or harmful because social media platforms have disrupted our communication systems. These intuitions have not been fully developed in democratic theory. This article builds on systemic approaches to deliberative democracy to characterize key vulnerabilities of social media platforms that disinformation actors exploit, and to clarify potential anti-deliberative effects of disinformation. The disinformation campaigns mounted by Russian agents around the United States’ 2016 election illustrate the use of anti-deliberative tactics, including corrosive falsehoods, moral denigration , and unjustified inclusion . We further propose that these tactics might contribute to the system-level anti-deliberative properties of epistemic cynicism, techno-affective polarization , and pervasive inauthenticity . These harms undermine a polity’s capacity to engage in communication characterized by the use of facts and logic, moral respect, and democratic inclusion. Clarifying which democratic goods are at risk from disinformation, and how they are put at risk, can help identify policies that go beyond targeting the architects of disinformation campaigns to address structural vulnerabilities in deliberative systems.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".