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Record W2950936934 · doi:10.4337/9781788977999.00020

Copyrightability of remixes and creation of remix rights

2019· book-chapter· en· W2950936934 on OpenAlexaboutno aff
Yahong Li

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2019
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsCopyright lawThe artsRemunerationCreativityPolitical scienceFair useThe InternetIntellectual propertyLaw and economicsSociologyLawMedia studiesComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Digital and Internet technologies have fostered the culture of remix. From literature, arts to music, remix has become a dominant force of creation. However, the legal status of remix remains obscure, akin to that of an illegitimate child who has a prominent existence but no clear legal right, to be caught between copyright holders and social media, being sued by the former and exploited by the latter. The existing fair use regime including the Canadian model of UGC exception has failed to provide a remedy due to its uncertainty and defensive nature. So have voluntary licensing schemes. Copyright law should be reformed to protect remix and to grant a positive right of remix to remixers, while obligating them to pay attribution and remuneration to copyright holders of the source materials, and to grant the same right to future remixers so that the societal creativity can be unleashed through more remixes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.463
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.206
Teacher spread0.186 · 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.

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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