Bibliographic record
Abstract
Like fair dealing (Section 29), Section 30.1 of the Copyright Act, known as the “Management and maintenance of collection” exception, places certain software preservation activities by libraries, archives, and museums (LAMs) outside the scope of copyright. Section 30.1 is similar to fair dealing in that it allows LAMs to engage in software preservation activities without permission from rightsholders. Unlike fair dealing, which the Supreme Court of Canada has defined as a broad and flexible user’s right that could apply to a wide variety of uses, [see paragraphs 30-32 of Theberge and paragraph 48 of CCH) the rights granted by Section 30.1 apply to preservation activities directly and have statutorily specified eligibility requirements, limitations, and procedures. Nevertheless, it is important to understand the baseline that Section 30.1 provides to LAMs engaging in the preservation of software. Section 30.1 identifies types of lawful copying that do not require permission from rightsholders. The activities that 30.1 permits do not encompass all copying that may be necessary to preserve and maintain access to software collections, and are subject to limitation. Therefore, it is advisable to read this guide alongside SPN’s Best Practices for Fair Use in Software Preservation, the situations, principles and limitations of which are transferable into the Canadian context of fair dealing.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.147 | 0.139 |
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".