The Copyright Implications of Book Editing APPs: Case Study — Story Surgeon
Bibliographic record
Abstract
English Abstract: There is a new type of software app currently being developed that allows the purchaser of an ebook to make edits to the content of the book by creating (or using existing) “filter” files. This article analyzes Story Surgeon, one such app in development. The copyright implications of such an app are myriad. Although Story Surgeon’s clever architecture may serve to shield it from copyright liability in the United States, Canadian copyright law may offer some unique challenges (and opportunities) for book editing apps like this one north of the border. This article examines the Canadian copyright implications of this new technology by canvassing such issues as moral rights, enabling infringement, the user-generated content (UGC) exception, and fair dealing for the purposes of criticism and review, parody, satire, and education. French Abstract: Un nouveau type d’applications permet actuellement a l’acheteur d’un livre electronique de modifier le contenu du livre en creant des fichiers « filtres » (ou en utilisant ceux qui existent deja). La presente etude de cas analyse Story Surgeon, une application presentement en developpement. Les repercussions d’une application semblable sur le droit d’auteur sont innombrables. Bien que l’habile architecture de Story Surgeon puisse servir a mettre le concepteur a l’abri des obligations en matiere de droit d’auteur aux Etats-Unis, la loi canadienne sur le droit d’auteur peut presenter des difficultes (et des possibilites) uniques pour les applications de modification de livres electroniques comme celle-ci. L’etude de cas porte sur les repercussions de cette nouvelle technologie sur le droit d’auteur au Canada en examinant des questions comme les droits moraux, la contrefacon, l’exception du contenu genere par les utilisateurs et l’utilisation equitable a des fins de critique/compte rendu, de parodie, de satire et d’education.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".