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Record W1603708681

Was the Internet Ever Neutral

2006· article· en· W1603708681 on OpenAlexaff
Craig McTaggart

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsTelus (Canada)
Fundersnot available
KeywordsNet neutralityThe InternetBusinessInternet transitPeeringStylized factInternet privacyComputer scienceInternet accessEconomicsWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

A new net neutrality rule cannot be justified as simply a codification of “the way the Internet has always been.” If the Internet was ever predominantly ‘neutral,’ it was at a time when the public was not allowed to use it. Since then, the requirements of Internet users have necessitated changes to many aspects of the Internet’s design and operation, with many of those changes requiring divergence from the Internet’s early customs and architecture. The examples of non-neutrality explored in this paper – preferential content arrangements, distributed computing, filtering and blocking to control network abuse, differential interconnection and interconnectivity, and the impact of resourceintensive applications and users – demonstrate that the Internet and its use are far from neutral or egalitarian. Those advocates who would like to see the Internet forced into the mould of a regulated public utility bear the heavy onus of justifying rejection of competitive market outcomes in favour of a stylized vision of public internetworking that prohibits or reduces the incentives for innovation within the network itself. The types of uses to which users are increasingly putting the Internet, as well as the subject-matter of current architectural research, suggest that the incongruity of a net neutrality rule with the interests of mainstream Internet users will only continue to grow. Instead of trying to prejudge what kinds of data service offerings consumers will find attractive in the future, the user-driven evolution of the Internet should be allowed to continue.

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 score0.271
Threshold uncertainty score0.453

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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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