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Record W4226380068 · doi:10.47678/cjhe.v51i4.189159

What Should Students Pay for University Course Readings?: An Empirical, Economic, and Legal Analysis

2021· article· en· W4226380068 on OpenAlexvenueaboutno aff
John Willinsky, Catherine Baron

Bibliographic record

VenueCanadian Journal of Higher Education · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
FundersBoettcher Foundation
KeywordsSyllabusReading (process)PublishingScope (computer science)Mathematics educationSociologyLegislatureComputer sciencePolitical sciencePedagogyPsychologyLaw

Abstract

fetched live from OpenAlex

The digital transformation of knowledge dissemination and academic publishing have sparked copyright disputes in the educational sector related to the scope of fair dealing. This study contributes (a) an empirical basis for such discussions by analyzing 3,391 course syllabuses (2015–2020) from 34 Canadian universities, and (b) a potential resolution to the disputes to which this analysis is applied. Among the reading types, 26.6% of the syllabuses had readings from academic sources, while 8.3% of the syllabuses had media articles and trade book chapters (with some overlap). The syllabus data are used to calculate a per-page royalty charge, which is used to demonstrate a proposed three-step syllabus rule to avoid double-chargingstudents for academic materials (amounting to 90.1% of readings by pages), while fairly compensating professional authors and their publishers (9.9% of readings by pages). The three-step syllabus rule provides a sound rationale for charging each student $1.40 per year to cover royalty charges for readings assigned in Canadian university courses.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.315
Teacher spread0.269 · 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.

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
Published2021
Admission routes2
Has abstractyes

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