Fair Use As an Advance on Fair Dealing? Depolarizing the Debate
Bibliographic record
Abstract
Over the last dozen or so years, countries with laws based on the Copyright Act 1911 (UK) have prioritized the issue of copyright exceptions in their law reform agendas. In each of these countries, a central question has related to the desirability of injecting greater flexibility into exceptions, most notably through the introduction of a “fair use” provision in addition to, or perhaps replacing much of, the existing closed-list system. The resulting statutory reforms have varied. Sri Lanka and Israel, for example, have both adopted a US-style fair use defense, along with a small number of specific exceptions, in their new copyright laws of 2003 and 2007. Singapore has also enacted an open-ended provision, albeit in the form of extended fair dealing rather than fair use. This was achieved by amending one of the purpose-limited fair dealing exceptions to allow it to apply to (almost) any use, with the many closed-list exceptions otherwise retained. In contrast, the reforms of Canada and the UK have – in terms of drafting choices – stayed closer to the existing infrastructure, with the addition of new fair dealing purposes directed to education, parody, and (in the UK) caricature, pastiche and quotation, and the introduction of new detailed exceptions to accommodate specific practices such as user-generated content, private copying, and data mining. The Australian reform experience has been less fruitful. After some expansion of exceptions (albeit within the closed-list model) in 2006, an impasse arose following strong calls for fair use from two major law reform inquiries. Despite the level of attention already given to copyright exceptions, the Australian government initiated yet another round of consultation in relation to reform options in 2018. There may finally be some progress, as the Australian government announced in August 2020 that it intends to make a series of reforms to the Australian copyright statute, including a new fair dealing exception for non-commercial quotation, a limitation on liability for use of orphaned works, and reforms to certain specific exceptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".