Net Neutrality Law: Past Policy, Present Proposals, Future Regulation?
Bibliographic record
Abstract
Network neutrality is a growing policy controversy. Traffic management techniques affect not only high-speed, high-money content, but by extension all other content too. Internet regulators and users may tolerate much more discrimination in the interests of innovation. For instance, in the absence of regulatory oversight, ISPs could use Deep Packet Inspection (DPI) to block some content altogether, if they decide it is not to the benefit of ISPs, copyright holders, parents or the government. ISP blocking is currently widespread in controlling spam email, and in some countries in blocking sexually graphic illegal images. In 1999 this led to scrutiny of foreclosure of Instant Messaging and video and cable-telephony horizontal merger. Fourteen years later, there were in 2013 net neutrality laws implemented in Slovenia, the Netherlands, Chile and Finland, regulation in the United States and Canada, co-regulation in Norway, and self-regulation in Japan, the United Kingdom and many other European countries. Both Germany and France in mid-2013 debated new net neutrality legislation, and the European Commission announced on 11 September 2013 that it would aim to introduce legislation in early 2014. This paper analyses these legal developments, and in particular the difficulty in assessing reasonable traffic management and ‘specialized’ (i.e. unregulated) faster services in both EU and US law. It also assesses net neutrality law against the international legal norms for user privacy and freedom of 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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 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".