The common law tort of appropriation of personality in Ontario.using legal transplant to solve the problem of the image rights lacuna in UK law.
Bibliographic record
Abstract
Abstract The image rights of athletes and celebrities are worth considerable sums of money to celebrities, brands, and sponsors alike. The desire to have celebrities endorse goods or services has meant that celebrities can benefit financially from selling and promoting their image. However, there is no image right in UK law. This is in spite of the fact that image rights exist in the practical sense, for example, in standard sports contracts. Thus, the courts have used varying degrees of judicial creativity to provide remedy when faced with image rights invasions, namely through passing off and breach of confidence actions. However, in the absence of image rights legislation in Ontario, the courts have also employed a degree of creativity, this time creating a free standing tort of appropriation of personality, specifically designed to deal with image rights invasions. Thus, in the absence of UK Parliamentary desire to legislate for image rights, this paper analyses whether it is possible to employ a ‘common law legal transplant’ by adopting the Ontario approach within the UK common law. This would provide a specific remedy, rather than circumventing the traditional intellectual property remedies which were not designed to deal with the issues image rights invasions create.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".