Copyright and open norms in seven jurisdictions: Benefits, challenges & policy recommendations
Bibliographic record
Abstract
This report explores the adoption, use and impact of open norms, as introduced in seven jurisdictions. In particular, the project’s aim was to understand the following: (a) motivations for the adoption of an open norm; (b) how a country has transitioned to an open norm; (c) the benefits and challenges of adopting an open norm; (d) impact on technology, education, research and library sector as relevant; (e) interpretation of open norms by the judiciary; and \n(f) adoption and use of an open norm, during the COVID-19 pandemic. \n \nTo achieve this, the countries were divided into two categories (mixed and civil legal systems) before presenting them in alphabetical order within the report. As such, under mixed/hybrid legal systems, Canada, Israel, Singapore and Sri Lanka are explored whilst Japan and South Korea are \nconsidered as examples of civil jurisdictions. The report commences with an assessment of the USA’s fair use doctrine before moving on to the other jurisdictions. \n \nApplying various criteria for measuring success and through an analysis of the law as well as engagement with National Experts of the relevant countries, the authors demonstrate that introducing an open norm has several benefits. These include, for example, allowing a country’s creative, educational and research sectors to progress effectively, and benefit from developments in technology in a timely manner. In particular, the report highlights the benefits experienced by countries such as Canada, Israel, Singapore and Japan, whilst the benefits of the USA’s long-standing fair use doctrine have also been captured. Where there have been challenges, these have not been due to the introduction of an open norm per se, but, rather due to failings in drafting the legislation (Sri Lanka) or how it has been approached by the judiciary (South Korea). As such, it must be emphasised that the challenges faced by Sri Lanka and South Korea emerged not due to any incompatibility of open norms with a hybrid or civil law system, but rather due to the reasons \nas outlined above. These are clear lessons that can be learnt by countries wishing to adopt an open norm in the future. As highlighted in the conclusions, challenges associated with legal transplants can be mitigated by varying different strategies including producing guidelines, opinions from legal authorities and paving the way for further regulations as seen in countries such as Israel and South Korea. \n \nAccordingly, the authors recommend the adoption of open norms in other countries around the world, including in European countries. As discussed in detail in this report, there is much to gain and little to lose by adopting open norms in copyright law. Rather than waiting for long periods for a piece of legislation to be introduced that addresses a single issue, open norms present the opportunity for countries to progress their education, research, creative and technological sectors in a timely fashion.
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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 | 0.057 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".