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Record W2276290261 · doi:10.22230/cjc.2016v41n1a2894

Telecom Responsibilization: Internet Governance, Surveillance, and New Roles for Intermediaries

2016· article· en· W2276290261 on OpenAlexaffvenue
Mike Zajko

Bibliographic record

VenueCanadian Journal of Communication · 2016
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIntermediaryInternet governanceThe InternetCorporate governanceBusinessCivil societyService providerEnforcementState (computer science)PoliticsInternet Architecture BoardPublic relationsLaw enforcementInternet service providerService (business)Internet privacyPublic administrationPolitical scienceLawMarketingFinance

Abstract

fetched live from OpenAlex

This article foregrounds internet intermediaries as a class of actors central to many governance and surveillance strategies, and provides an overview of their emerging roles and responsibilities. While the growth of the internet has created challenges for state actors, state priorities have been unfolded onto the private institutions that provide many of the internet’s services. This article elaborates responsibilization strategies implicating internet intermediaries, and the goals that these actors can be aligned toward. These include enrolling telecom service providers in law enforcement and national security-oriented surveillance programs, as well as strategies to responsibilize service providers as copyright enforcers. But state interests are also responsive to pressures from civil society, so that “internet values” are increasingly channelled through the formal political processes shaping internet governance.

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)0.007
metaresearch head score (Gemma)0.007
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 score0.013
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.021
Scholarly communication0.0130.015
Open science0.0010.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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