De Minimis Copying, MOOCs & Institutional Risk Avoidance
Bibliographic record
Abstract
This presentation examines institutional risk avoidance in relation to copyright concerns and MOOCs. Specifically, the presentation focuses on a case study of the Understanding Video Games MOOC developed at the University of Alberta. As part of the development of the MOOC the instructional team desired to put short video clips (10-20 seconds) of video games into the MOOC for educational purposes; however, the University insisted that this could only be done with permission of the rights holder, despite both de minimus and fair dealing arguments put forward for the inclusion of the short clips. \n \nThe presentation examines the arguments from both sides (instructor and administration) as well as including some discussion of rights holders’ responses to requests for permission. The presentation argues that pervasive risk avoidance by University administrators and a lack of clear guidance around substantiality thresholds, particularly in relation to video games, along with the concerns of rights holders served to undermine the pedagogical goals of the MOOC. In conclusion, University copyright offices and users’ rights proponents need to more firmly underscore the importance of de minimis copying in relation to copyright concerns.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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, 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".