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Record W2121011887

Net Neutrality Law: Past Policy, Present Proposals, Future Regulation?

2013· article· en· W2121011887 on OpenAlexaboutno aff
Christopher T. Marsden

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsNet neutralityLegislationDeep packet inspectionBusinessNeutralityCommissionGovernment (linguistics)The InternetEuropean unionScrutinyLawPolitical scienceInternational tradeComputer securityNetwork packet
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.227
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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