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Record W2926899119 · doi:10.26522/ssj.v13i1.1845

Chokepoints: Global Private Regulation on the Internet

2019· article· en· W2926899119 on OpenAlexaffvenueabout
Sara Bannerman

Bibliographic record

VenueStudies in Social Justice · 2019
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsThe InternetBusinessInternet privacyLaw and economicsEconomicsComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Google, eBay, GoDaddy and PayPal -intermediaries that now control huge swaths of the internet's search, marketplace, advertising, domain name, and payment activities -have become some of the leading threats to prospects for social justice.There have been significant victories in recent battles over the regulation of the internet, such as the 2012 "internet blackout" that stopped proposed American legislation that would have left web sites carrying intellectual property-infringing content vulnerable to total shutdown.Such victories are pyrrhic, as Natasha Tusikov's (2016) important book shows; effectively the same measures to shut down web services have been put in place anyway, without legislative oversight, through informal agreements between intellectual property owners, government officials, and internet intermediaries.Tusikov reveals that eight such agreements now regulate the internet, having been established privately through back room deals.Tusikov ( 2016) reveals in Chokepoints: Global Private Regulation on the Internet, that efforts to shut down Wikileaks by cutting off payment and domain services to the site launched a new phase in internet regulation; legislation would no longer be the primary tool of enforcement.Rather, a new wave of back room deals saw intermediaries agree to act as enforcers for corporate intellectual property owners.Through 90 interviews with government officials, corporate actors, and civil society groups in the US, UK, Australia, and Canada, Tusikov traces the establishment of the secret

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score0.341

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 science0.0000.001
Research integrity0.0000.000
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 head0.049
GPT teacher head0.341
Teacher spread0.291 · 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
Published2019
Admission routes3
Has abstractyes

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