A Response to Fake News as a Response to Citizens United
Bibliographic record
Abstract
This short essay takes aim at a core assumption of Citizens United v FEC that there is no such thing as too much speech. Courts use this argument to justify non-intervention in cases of politically protected speech. If fake news is political, then it is hard to regulate. Yet, if politically protected speech is an absolute right, never to be infringed, it is possible to invalidate the assumption that the right can be enforced. The idea borrows from the Church Turing thesis and sets up a logical contradiction in systems of absolute rights. Drawing a parallel to the absence of absolute truth in systems of logical statements, one can show a self-contradiction in a system of absolute rights. From this, it follows that there is a condition of too much speech, for which regulation is justified, even as applied to politically protected speech. Setting aside all the usual exceptions, such as incitement to violence, fraud, and defamation, the need for intervention holds even for the truest most desirable speech. This implies a tighter boundary for regulation than courts have previously recognized. If true, the case for regulating false speech is, a fortiori, stronger still. Thus fake news should be easier to regulate than current laws admit. The conclusion also has direct application to Super PAC spending. In effect, certain boundaries on speech actually free the market for speech.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".