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Record W4413363474 · doi:10.17161/jcel.v8i1.23060

Assessing the use of Canadian Literature in teaching at Simon Fraser University

2025· article· en· W4413363474 on OpenAlexaffabout
Jennifer Zerkee, Donald Taylor

Bibliographic record

VenueJournal of Copyright in Education & Librarianship · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSociologyCanadian literatureMathematics educationPsychologyMedia studiesArtLiterature

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.075
GPT teacher head0.292
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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