Of Pride and Prejudice: Mangled Art, Mutilated Statues, Hypersensitive Authors and the Moral Right of Integrity
Bibliographic record
Abstract
The issue of how much protection should be given to the so-called ‘moral rights’ of artists has intrigued and engaged scholars and policymakers for decades. Although the moral right of integrity seeks to protect an artist’s work from ‘derogatory treatment’, there is no universal consensus on the standard by which ‘derogatory’ is to be defined, and the appropriate test for ‘prejudice to reputation or honour’ remains a question of some uncertainty. In particular, it is not entirely clear whether mere offence on the author’s part is sufficient or whether objective harm to the author’s reputation is required. This article seeks to endorse the objective test adopted in the United Kingdom (‘UK’) and other common law jurisdictions for ‘prejudice to reputation or honour’ in relation to the moral right of integrity, but argues that a strict objective standard that is rigidly applied to all works can lead to unfairness in some cases, and should be tempered by a doctrine of ‘presumed prejudice’ for works of tangible visual art that have been modified without authorisation. Of the ‘common law’ jurisdictions compared in this article — the UK, Canada and Australia — only Canada currently prescribes a framework of ‘presumed prejudice’ for modified works of tangible art in its domestic copyright legislation. This article explores the feasibility of transplanting the Canadian framework for presumptive prejudice into the Australian copyright system, so as to bring about a fairer distribution of power between authors and users of visual artwork.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.079 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| 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".