Assessing the use of Canadian Literature in teaching at Simon Fraser University
Bibliographic record
Abstract
For over a decade, members of Canada’s creative industries have claimed that Canadian post-secondary institutions are copying and using content without adequately compensating creators; these campaigns have primarily focused on fiction authors. This study aims to address these claims by determining how much Canadian creative literature is actually being used in a representative Canadian university. We analyzed reading materials provided to students as library reserves, textbooks, and course packs for the periods 2010-2012 and 2018-2022 and found that across both periods approximately 1.3% of courses assigned Canadian creative works as readings. An analysis of only the Fall semesters across these periods found that approximately 0.7% of all works – that is, copied excerpts and uncopied (purchased) works – assigned via library reserves, textbooks, and course packs were Canadian creative works. The number of assigned readings that included copied Canadian creative works (generally consisting only of course packs, not textbooks and likely not library reserves) would comprise much less than 0.7%. Therefore, this research suggests that the use and specifically copying of Canadian creative content in Canadian universities is not substantial enough to result in significant potential remuneration for the copying of an author’s work.
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 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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".