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Record W2623314048 · doi:10.17161/jcel.v2i1.7159

Teaching an Invisible Subject: How are we Educating Faculty about Copyright?

2018· article· en· W2623314048 on OpenAlexafffundabout
Jennifer Zerkee

Bibliographic record

VenueJournal of Copyright in Education & Librarianship · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsCopyright lawPublic relationsSubject (documents)Political scienceHigher educationCopyright ActAction (physics)Intellectual propertyLawLibrary scienceComputer science

Abstract

fetched live from OpenAlex

Copyright can be an invisible issue for instructors because infringement or improper use of copyright-protected material will not impede teaching. Copyright law is nuanced and open to interpretation; it is not always clear whether a particular action is compliant or not. This poster will share the results of the presenter’s Canada-wide survey of university copyright administrators, exploring institutions’ provision of copyright education to instructors. The presenter found more questions rather than answers as a result of the survey. Most respondents do no assessment of their copyright instruction, and instead are comfortable relying on experience, questions from faculty, and anecdotal evidence to form an impression of instructors’ familiarity with copyright rules. Is informal appraisal adequate for ensuring that libraries and copyright offices are fulfilling their responsibility to encourage and enable the confident and lawful use of copyright-protected material? What other evidence could be gathered to inform copyright administrators’ efforts? This poster will encourage participants to think about copyright education at their institutions, will share the results of the survey, including approaches being taken by universities across Canada, and will share Simon Fraser University's approaches to instructor education

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient 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: none
Teacher disagreement score0.633
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.010
Open science0.0010.000
Research integrity0.0000.001
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 head0.100
GPT teacher head0.421
Teacher spread0.321 · 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
Published2018
Admission routes3
Has abstractyes

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