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Record W4416023739 · doi:10.17161/jcel.v8i1.23066

Walking the Talk: Establishing Best Practices for Attributing and Licensing Employee-Created Works

2025· article· en· W4416023739 on OpenAlexaffabout
Heather Martin, Ali Versluis

Bibliographic record

VenueJournal of Copyright in Education & Librarianship · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBest practicePresentation (obstetrics)CreativityIntellectual propertySubject (documents)Social media

Abstract

fetched live from OpenAlex

Library employees routinely create content that is subject to copyright, ranging from web pages to video tutorials to photographs to social media posts. In most cases these contributions are invisible, as the creativity and intellectual effort of employee creators is typically unacknowledged. At the University of Guelph, we endeavoured to bring the works of employees into the spotlight by providing attribution on public-facing content whenever possible, while also facilitating downstream uses of those works through the use of open licenses. In doing so, we hoped to address a general lack of awareness and understanding of copyright and model respectful copyright practices for library employees and users alike. However, establishing and implementing these new copyright-focused practices was not without challenge and controversy. This paper – which builds upon a presentation we delivered at the 2024 ABC Copyright Conference (Martin & Versluis, 2024) – explores the obstacles we encountered in our multi-year journey to develop practices that were acceptable to content creators and content managers, while also respecting the boundaries of institutional intellectual property policies and collective agreements. Keywords: copyright literacy, staff development, copyright ownership, employee created works, library policy and documentation

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.008
Open science0.0000.000
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 head0.056
GPT teacher head0.304
Teacher spread0.248 · 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
Published2025
Admission routes2
Has abstractyes

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