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Record W2800206334 · doi:10.5931/djim.v14i0.7852

Managing Copyright in Digital Collections: A Focus on Creative Commons Licences

2018· article· en· W2800206334 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDalhousie Journal of Interdisciplinary Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCommonsIntellectual propertyPublic relationsPublic domainDigital rights managementCopyright infringementBusinessPolitical scienceWorld Wide WebInternet privacyLawComputer science

Abstract

fetched live from OpenAlex

Digital collections in public institutions can benefit from Creative Commons licenses, as they allow the responsible sharing and use of information online by faculty, students, researchers, and the public at large. This essay outlines the proper management of Creative Commons licenses in the following order: first, the current state of copyright in Canada; second, how the Creative Commons functions and its relation to free culture and Open Access; third, Creative Commons for public institution collections, and not just as a holding body, but as a repository; fourth, tools for managing Creative Commons licences online, including digital rights management (DRM) and technological protection measures (TPMs); and fifth, future impacts of the Creative Commons on digital collections. Creative Commons licences offer libraries that opportunity to expand their patronage and explore broader uses of their collections.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
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
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.018
GPT teacher head0.261
Teacher spread0.243 · 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