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Record W4210873251 · doi:10.1017/s0008197321001045

REMARKS ON TECHNOLOGICAL NEUTRALITY IN COPYRIGHT LAW AS A SUBJECT MATTER PROBLEM: LESSONS FROM CANADA

2022· article· en· W4210873251 on OpenAlex
Abraham Drassinower

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Cambridge Law Journal · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeutralityCopyright lawPropositionSubject matterExpression (computer science)Subject (documents)Fair useNet neutralityLaw and economicsLawLiabilityIntellectual propertyCorollaryFair dealingNothingDigital Millennium Copyright ActPolitical scienceSociologyComputer scienceThe InternetEpistemologyPhilosophyMathematics

Abstract

fetched live from OpenAlex

Abstract The paper argues that the principle of technological neutrality in copyright law is best grasped, not as a policy-driven imperative seeking to adapt copyright law to the exigencies of the digital environment, but rather as a principle immanent in the modern concept of copyright subject matter providing that merely technical or non-expressive uses of works of authorship do not attract liability. Technological neutrality is but a corollary of originality; that is, of the elementary proposition that copyright law protects original expression and nothing but original expression.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.020
GPT teacher head0.223
Teacher spread0.203 · 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