Same issues, different treatment: common law and civilian approaches to the copyright treatment of freelance works
Bibliographic record
Abstract
Publishers exploit freelance authors' works not only in print but also digitally, through their own websites or by selling them to third party databases. Freelancers have launched a series of copyright infringement cases against publishers of newspapers and magazines. This paper will argue that the issue of which party should own and control digital exploitation rights is treated differentially across jurisdictions; North American courts struggle to apply vague and seemingly 'neutral' copyright law provisions, whereas the continental European courts apply express legislation. Continental European courts are more equipped to resolve such issues, and in tandem with necessary legislative reform, North American courts may do well to consider some of the civilian approaches in common law decision-making.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.076 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".