Copyright Anxiety and Legal Chill in Higher Education: A Comparison of Canada and the United Kingdom (UK)
Bibliographic record
Abstract
This study builds upon and extends previous research into the phenomenon of copyright anxiety, initially measured through the Copyright Anxiety Scale (CAS) developed by Wakaruk et al. (2021). The primary aims are to explore levels of copyright anxiety within the higher education sectors of the UK and Canada, and to examine whether copyright law and the way it is perceived in these sectors inhibits innovative research and teaching practices. Using an adapted version of the copyright anxiety scale survey, we collected responses from over 500 participants in the UK and Canada during the summer of 2023. Additionally, we conducted seven focus groups with 32 individuals to gain deeper insights into the phenomenon and explore potential interventions. Our findings indicate that those working in higher education are more worried about copyright than those outside the sector. Copyright concerns can cause significant anxiety and emotional labor, which may lead to legal chill that hampers teaching, research, and the provision of library programs and services. For example, academics may use less appropriate materials due to copyright concerns, negatively affecting pedagogical impact. Librarians, often acting as copyright advisors, may experience heightened anxiety, leading them to provide more risk-averse guidance to users and decision-makers. Future publications from this research will further develop a coding frame and explore options for mitigating copyright anxiety and chill in this sector.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".