Bibliographic record
Abstract
Computer programs are protected under copyright law in most developed countries. Software piracy, resulting from outright copying of a substantial part of software code, will generally be an infringement of copyright. More problematical and contentious issues can arise when software is copied with the intention of producing interoperable or competing products through a process of reverse engineering the original software. This can often be the only way the underlying ideas in computer programs can be revealed, particularly when the software is made available only in machine-readable object code. Issues involving reverse engineering and copyright infringement have been most developed in the United States, where a liberal approach has been taken, under the doctrine of copyright fair use. Under US law, existing software can be copied and reverse engineered to enable compatible and competing programs to be developed, provided that a competing product can be regarded as 'transformative'. Other jurisdictions, including the EU and Australia, have introduced specific legislation to provide narrow exceptions to copyright infringement of software, through reverse engineering, but only to accommodate interoperability. In other countries, including Japan, Canada and Singapore, the legislative framework is less developed, leaving issues involving reverse engineering and copyright to be resolved under existing fair dealing laws.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".