LAURA MILLAR, The Story Behind the Book: Preserving Authorsâ and Publishersâ Archives
Bibliographic record
Abstract
As the foregoing examples demonstrate, the books differ in approach, tone, and purpose, and each has different strengths.Harris aims to provide a comprehensive, concise, and objective overview of Canadian copyright law.Murray and Trosow do not claim to be comprehensive; instead, they cover selected topics, often in some depth, although they fall short on the details of the law.In some cases, Harris provides more detail.Nor do Murray and Trosow claim to be objective -their stance is clearly pro-user, and they are prepared to state their opinions and speculate about the interpretation of certain provisions in new ways that make more copyright-protected material legally available for use.Rapid technological change has completely altered the copyright landscape, and applying copyright in the digital environment continues to be a challenge.Information professionals ignore copyright at their peril.For that reason, both books deserve a place on the Canadian information professional's bookshelf.One can never have too many copyright resources readily available, and these new editions are a welcome and accessible addition to support our understanding of copyright.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".