Controlled Digital Lending (CDL): A Panel to Discuss Legal and Practical Considerations Involved in the Implementation of CDL by Public and Post-Secondary Libraries in Canada
Bibliographic record
Abstract
Controlled Digital Lending (CDL) is a model under which a library would digitize physical books in its collection, including in-copyright works, and lend out the digital version to one user at a time, for a limited time, using appropriate safeguards.  We propose a panel to discuss legal and practical considerations involved in the implementation of CDL by public and post-secondary libraries in Canada.\nMany books in post-secondary and public libraries remain underutilized because they only exist in physical form sitting on the shelves. CDL can help make library collections accessible to a broader community.  Its benefits include breathing new life into older and difficult to find books, many of which are out-of-print, as well as making a breadth of information resources and knowledge available to anyone with internet access, regardless of the reader’s physical location, physical ability, or economic status.\nHowever, one of more significant perceived barriers to making physical books available in digital format through CDL is copyright law.\nThis panel will discuss the prospective legal and practical considerations arising from implementing CDL at libraries in Canada.  This discussion will include the extent to which fair dealing or other exceptions in the Copyright Act can reasonably support the digitization and controlled lending of those digitized versions of in-copyright books, as well as what safeguards and best practices might appropriately be applied to such a model to better manage risk.\nAdrian Sheppard – Director, Copyright Office, University of Alberta\nLila Bailey – Lila is Policy Counsel for Internet Archive where she advises the organization on the complex legal and policy issues associated with democratizing access to knowledge. Prior to this, Lila was a solo practitioner specializing in digital copyright and supporting individual entrepreneurs and creators, early stage start-ups, Internet platforms, and libraries. Lila began her working life in traditional publishing at Conde Nast Publications, but decided to go to law school so that “the lawyers wouldn’t break the Internet.” Since then, she has dedicated her career to public interest technology law, and has worked to increase access to knowledge at Creative Commons, the Electronic Frontier Foundation, the William and Flora Hewlett Foundation, and the Samuelson Law, Technology and Public Policy Clinic at Berkeley Law. Lila has a JD from Berkeley Law and a BA in Philosophy from Brown University.\nAriel Katz – Associate Professor, Faculty of Law, University of Toronto\nAndrea Mills – Digitization Program Manager, Internet Archive Canada\nGraeme Slaght Scholarly Communication & Copyright Outreach Librarian, University of Toronto
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".