What Should Students Pay for University Course Readings? An Empirical, Economic, and Legal Analysis
Bibliographic record
Abstract
In the context of the significant court battles that are being fought over the potential copyright infringement involved in distributing the articles and excerpts assigned to students in university courses, this study analyzes 3,391 course syllabuses (2015-2020) from nine provinces and 34 universities across Canada. It identifies the types and proportions of required readings among academic and non-academic sources. Academic readings are assigned on 26.6 percent of the syllabuses, compared to 8.3 percent of syllabuses for media articles and trade book chapters. Among the assigned readings, journal articles lead the list (with 54.3% of all readings), compared to scholarly book chapters (33.5%), media articles (6.0%), and trade book chapters (6.3%). The social sciences lead in the assignment of journal articles and the humanities in trade book chapters, while science was least likely to have assigned readings of any type. The study also found that textbooks are required on a majority of syllabuses (66.0%), with only minor differences in this proportion across science, social sciences, and humanities. The data enable a further analysis at the page level of what the average student is asked to read annually, which, at the Access Copyright current tariff of $14.31 (approved by the Canadian Copyright Board), amounts to a $0.021 per page. This rate is applied in a proposed new “three-step syllabus rule” that avoids double-charging students for academic materials (90.1% of readings by pages), while fairly compensating professional authors and their publishers (9.9%), with the data analyzed here suggesting a $1.40 annual charge per student for their assigned readings. Version 2 of this preprint contains an appendix setting out how the Open Syllabus Project can be used with the three-step syllabus rule.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".