Section 230 of the Communications Decency Act of 1996: The Antiquated Law in Need of Reform
Bibliographic record
Abstract
Imagine a world without the information sharing-giants of Facebook, YouTube, Instagram, TikTok, Twitter, or Reddit. It is nearly impossible to go a day without some type of exposure to content created, published, and shared on such platforms. A world without these platforms would bear a striking resemblance to the world in 1996, when Congress passed the Communications Decency Act (“CDA”). The CDA, often referred to as “the 26 words that made the internet,” states that “[n]o provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider.” In short, the CDA is a federal law that prevents websites, blogs, forums, and other sources of online information from being held liable for their users’ speech. The legal protections established in § 230 of the CDA are unique to the United States—European nations, Canada, Japan, and the vast majority of other countries do not provide such safeguards to internet companies. Despite the high levels of internet access in these countries, the largest and most prominent online services are located in the United States. Section 230 makes the United States desirable as a safe haven for internet providers who wish to provide controversial or politicized speech with a legal platform and environment which is favorable to free expression.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.027 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".