How copyright affects interlibrary loan and electronic resources in Canada
Bibliographic record
Abstract
n Canada, the Copyright Act, copyright collectives such as Access Copyright, and the terms of negotiated license agreements all affect library practice. This paper will discuss how interlibrary loan practices and access to electronic resources are influenced by these three factors. For example, the Copyright Act does not allow digital interlibrary loan. Neither does the recent interim Access Copyright tariff, but the 2004 CCH Supreme Court Judgment seems to allow libraries a path forward for digital interlibrary loan. How do Canadian libraries choose between these conflicting messages? One of the biggest licensing issues facing Canadian libraries are electronic licenses that require Canadian libraries to follow American rather than Canadian copyright law. Two such examples are the CONTU guidelines and section 108 (g) (2) of US Copyright Law. There is nothing equivalent to section 108 (g) (2) in Canadian law, yet it is not uncommon to find Canadian libraries that have signed licenses requiring them to follow this and other parts of American law. How do Canadian libraries deal with conflicting requirements between the license and Canadian law?
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Not applicable | low |
| gpt | Scholarly communication Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Other design | low |
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".