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Record W2143425863 · doi:10.19030/cier.v4i3.4115

Managing The Legal Risks Of High-Tech Classrooms

2011· article· en· W2143425863 on OpenAlexaffabout
Laura A. Nenych

Bibliographic record

VenueContemporary Issues in Education Research (CIER) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsThe InternetLegislationProcess (computing)ConfusionPublic relationsOrder (exchange)Educational technologyInternet privacyLawComputer scienceBusinessWorld Wide WebPolitical sciencePsychology

Abstract

fetched live from OpenAlex

When professors and students utilize the Internet, course web pages, and other online learning tools, much of the material that they make use of is protected by copyright law. A blend of case law and legislation governs the use of online materials and how technology can be used in the classroom and in school-related activities, often creating confusion for content users. Educators and students alike need to be familiar with applicable laws and need to understand the implications of common activities such as using technology in the classroom, conducting research on the Internet, and using multi-media in classroom presentations and student projects. Canadian copyright law is currently undergoing a much-needed process of reform, in order to bring Canadian copyright law in line with the laws of the rest of the world, and to keep up with rapidly changing technology that has changed the way people use copyrighted materials. This paper will focus on the legalities surrounding the use of technology and digital media in educational settings. It will provide guidelines for Canadian educators to ensure that their use of information technology and digital materials in the classroom is both appropriate and acceptable, and will propose strategies to manage those risks.

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 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: none
Teacher disagreement score0.743
Threshold uncertainty score0.996

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 communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.234
GPT teacher head0.391
Teacher spread0.157 · 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
Published2011
Admission routes2
Has abstractyes

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