MétaCan
Menu
Back to cohort
Record W1919898023 · doi:10.17645/mac.v3i2.270

The Copyright Surveillance Industry

2015· article· en· W1919898023 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedia and Communication · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnforcementHarmBusinessDigital Millennium Copyright ActCopyright infringementInternet privacyThe InternetFair useLaw and economicsIncentiveIntellectual propertyInformation goodCopyright lawComputer securityEconomicsLawPolitical scienceComputer scienceMarket economy

Abstract

fetched live from OpenAlex

Creative works are now increasingly distributed as digital “content” through the internet, and copyright law has created powerful incentives to monitor and control these flows.<strong> </strong>This paper analyzes the surveillance industry that has emerged as a result. Copyright surveillance systems identify copyright infringement online and identify persons to hold responsible for infringing acts. These practices have raised fundamental questions about the nature of identification and attribution on the internet, as well as the increasing use of algorithms to make legal distinctions. New technologies have threatened the profits of some media industries through copyright infringement, but also enabled profitable forms of mass copyright surveillance and enforcement. Rather than a system of perfect control, copyright enforcement continues to be selective and uneven, but its broad reach results in systemic harm and provides opportunities for exploitation. It is only by scrutinizing copyright surveillance practices and copyright enforcement measures that we can evaluate these consequences.

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.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score0.240

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 integrity0.0000.000
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 head0.046
GPT teacher head0.231
Teacher spread0.185 · 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