Monkey Selfie and Authorship in Copyright Law: The Nigerian and South African Perspectives
Bibliographic record
Abstract
A photograph taken by a monkey is in the centre of a copyright claim in the famous monkey selfie case in the United States of America. Suing as next friend of the monkey, named Naruto, the People for the Ethical Treatment of Animals contended that copyright in the photograph belongs to the monkey as author of the photograph since the monkey created the photograph unaided by any person. On the motion of the defendants, the case was dismissed by the US district court on the ground that the concept of authorship under US Copyright Act cannot be defined to include non-human animals. The dismissal order was confirmed by a three-judge panel of the US Court of Appeal of Ninth Circuit a request for an appeal before a panel of eleven judges of the appellate court was denied. This paper reviews the case in the light of the concept of authorship and ownership, with specific focus on the authorship of photographs, under the Nigerian Copyright Act and South African Copyright Act. In so doing, it examines and relies on Ginsburg's six principles for testing authorship to test the authorship of photographs under the Acts. It also relies on the concepts of subjective rights and legal personality to explain the implication of conferring copyright ownership on non-human animals. It argues that for authorship of and ownership of the copyright in a photograph to be established under the Nigerian Copyright Act and South African Copyright Act, a legal person must have created the photograph. Consequently, for the purposes of argument, the paper proceeds on the assumption that the monkey selfie case originated from Nigeria or South Africa. After analysing relevant statutory provisions and case law, the paper finds that the Nigerian Copyright Act and the South African Copyright Act do not envisage the conferral of authorship in particular, and copyright protection in general, to a non-human animal. It then concludes that the courts in both countries would not reach a different conclusion from the one made by the US courts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".