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Record W7115014548 · doi:10.1016/j.acalib.2025.103184

Exploring Copyright: A case study in practical copyright education for college educators

2025· article· en· W7115014548 on OpenAlexaffabout

Bibliographic record

VenueThe Journal of Academic Librarianship · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHigher educationCopyright lawIntellectual propertyElectronic learning

Abstract

fetched live from OpenAlex

An accelerated transition from in-person to online learning at Centennial College, an art and applied technology college in Toronto, Ontario, was catalyzed by the pandemic starting in early 2020. This change led to a significant rise in copyright inquiries to the library's copyright office, highlighting an immediate need for enhanced copyright education and support for effective application of copyright knowledge in online teaching environments. In response to this need, the Exploring Copyright series was created by the library's copyright librarian. This outer-space themed series consisted of eight copyright “missions” tailored for faculty and staff involved in online teaching and course development. The series was presented online in sessions delivered every other week over the course of a semester. The material was subsequently compiled into a course environment with asynchronous modules and resources. This paper will explore the evidence-based development, initial delivery, and evaluation of the Exploring Copyright series. It will examine how this initiative complemented the existing copyright literacy programs at Centennial College, addressing the evolving needs of the academic community. Copyright education and outreach professionals in higher education may find this approach particularly useful. The Exploring Copyright series offered a flexible, community-driven model for adapting copyright education to suit a rapidly changing online environment.

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)0.002
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.006
Open science0.0010.000
Research integrity0.0000.001
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 head0.181
GPT teacher head0.340
Teacher spread0.159 · 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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