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Record W7034238655

Submission to South African Parliament's Portfolio Committee on Trade and Industry - Re: Copyright Amendment Bill [B13B - 2017]

2021· article· en· W7034238655 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)PortfolioEconomic JusticeCopyright ActIntellectual propertyPublic policy
DOInot available

Abstract

fetched live from OpenAlex

This submission is on behalf of the Global Expert Network on Copyright User Rights. The Network is an association of over 100 copyright academics from over 30 countries who conduct research and offer technical assistance to governments and stakeholders on the reform of copyright limitations and exceptions to promote the public interest.\nProfessor Sean Flynn, Counsel of Record, is a former Law Clerk for the late Chief Justice Arthur Chaskalson, is currently a Senior Research Fellow at the University of Cape Town IP Unit, and has been conducting research and leading academic projects in South Africa for over two decades.\nMembers of the Global Expert Network on Copyright User Rights that were consulted on this submission include: Patricia Aufderheide, American University Carys Craig, Osgoode Hall Law School, York University, Canada Niva Elkin Koren, Tel-Aviv University Christophe Geiger, University of Strasbourg (France) Lucie Guibault, Schulich School of Law, Dalhousie University, Canada Peter Jaszi, American University Washington College of Law Ariel Katz, University of Toronto Thomas Margoni, Faculty of Law, KU Leuven João Pedro Quintais, Institute for Information Law, University of Amsterdam Allan Rocha, Federal University of Rio De Janeiro Matthew Sag, Loyola University of Chicago Pam Samuelson, Berkeley Law School Arul George Scaria, National Law University, Delhi Tobias Schonwetter, University of Cape Town IP Unit Martin Senftleben, Institute for Information Law, University of Amsterdam Peter Yu, Texas A&M School of Law \nREQUEST TO PRESENT AT PUBLIC HEARING\nWe request to be represented by our counsel of record, Professor Sean Flynn, at the public hearings on the Bill scheduled on Wednesday, 4 August, and Thursday, 5 August 2021.\nSUMMARY OF OUR COMMENTS\nWe provide this comment on Clause 13, section 12A of the Copyright Amendment Bill [B13B-2017]. Section 12A is an open general exception for “fair use” of copyrighted works. This provision is largely an updating of South Africa’s current general exception for “fair dealing” with a copyrighted work. The primary improvements of Section 12A over the current fair dealing exception are\n(1) to open the list purposes to which the exception can apply by virtue of including the words “such as” before the list of authorized purposes, and\n(2) providing an explicit balancing test to determine whether a particular use is fair.\nBenefits of the hybrid approach\nIn our view, the proposed fair use provision combined with the specific list of exceptions provides South Africa the “best of both worlds” combining openness and predictability. The open fair use exception makes the exceptions future-proof. It permits the law to adapt to new uses, technologies, and purposes which may not be anticipated in the specific exceptions. The list of specific exceptions in Section 12B provides a higher degree of predictability for the set of uses long authorized in South Africa copyright law.\nCompliance with international law\nThis hybrid approach to exceptions is fully compliant with international law. The so-called “three step” test does not prohibit open general exceptions that operate through case by case application of a specifically delineated balancing test. At least 11 countries have similar provisions in their law and none have been challenged.\nWe include below further explanations of each of these points.

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)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.987
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0100.003
Scholarly communication0.0130.005
Open science0.0030.004
Research integrity0.0280.014
Insufficient payload (model declined to judge)0.0730.043

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 head0.025
GPT teacher head0.275
Teacher spread0.250 · 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.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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