Promoting Knowledge: A Rationale for the Copyright Protection of Computer-Generated Works
Bibliographic record
Abstract
Artificial intelligence systems can now produce complex artistic and literary works without human authorial contribution. However, considering the absence of authorship, they are currently not covered by copyright law. In past decades, many scholars argued for their protection but did not thoroughly address their claim under a copyright rationale. Building on this literature, this paper will propose a more comprehensive policy framework for the copyrightability of computer-generated works. It will analyze the Canadian Copyright Act under the foundational principle of the advancement of knowledge, which lies in its economic purpose. This objective focuses primarily on the extrinsic features of works, superseding the ambiguous notion of creativity and thus relegating authorship to a secondary role. Since computer-generated works also share with the public “expressive” knowledge, this paper will suggest allocating exclusive economic rights to the persons who arrange them. It can incentivize these persons to contribute to the promotion of knowledge.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".