Teaching an Invisible Subject: How are we Educating Faculty about Copyright?
Bibliographic record
Abstract
Copyright can be an invisible issue for instructors because infringement or improper use of copyright-protected material will not impede teaching. Copyright law is nuanced and open to interpretation; it is not always clear whether a particular action is compliant or not. This poster will share the results of the presenter’s Canada-wide survey of university copyright administrators, exploring institutions’ provision of copyright education to instructors. The presenter found more questions rather than answers as a result of the survey. Most respondents do no assessment of their copyright instruction, and instead are comfortable relying on experience, questions from faculty, and anecdotal evidence to form an impression of instructors’ familiarity with copyright rules. Is informal appraisal adequate for ensuring that libraries and copyright offices are fulfilling their responsibility to encourage and enable the confident and lawful use of copyright-protected material? What other evidence could be gathered to inform copyright administrators’ efforts? This poster will encourage participants to think about copyright education at their institutions, will share the results of the survey, including approaches being taken by universities across Canada, and will share Simon Fraser University's approaches to instructor education
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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 | 0.047 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.032 |
| Scholarly communication | 0.037 | 0.034 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".