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Record W2140544070 · doi:10.1080/19331681.2013.843932

Speaking for Freedom, Normalizing the Net?

2013· article· en· W2140544070 on OpenAlexaff
Peter J. Smith

Bibliographic record

VenueJournal of Information Technology & Politics · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsAthabasca University
Fundersnot available
KeywordsIntellectual propertyLegislationBalance (ability)Political scienceLaw and economicsEuropean unionProperty (philosophy)International tradeResistance (ecology)LawPolitical economyBusinessSociologyPsychology

Abstract

fetched live from OpenAlex

This article focuses on the efforts of large intellectual property exporters, such as the United States, to curtail Net freedoms by means restrictive copyright and anti-circumvention measures through the Anti-Counterfeiting Trade Agreement (ACTA), other international agreements, and reforms of national legislation. These efforts have upset the historical balance between owners and users of copyright, leading to widespread global resistance. The defeat of the ACTA in Europe may represent the last hurrah of the U.S. and the European Union to globalize their norms of digital intellectual property rights. However, the U.S. and EU can still impose their standards on a bilateral basis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.039
Scholarly communication0.0170.022
Open science0.0010.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0210.004

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 head0.015
GPT teacher head0.218
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2013
Admission routes1
Has abstractyes

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