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Record W1801978711 · doi:10.24908/pceea.v0i0.4689

INTELLECTUAL PROPERTY (IP) FOR FUTURE ENGINEERS

2012· article· en· W1801978711 on OpenAlexvenueaboutno aff
Michel Loiselle, Dumitru Olariu

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyCompetence (human resources)Presentation (obstetrics)Agency (philosophy)EngineeringBusinessEngineering managementKnowledge managementManagementPolitical scienceComputer scienceSociologyMedicineEconomicsLaw

Abstract

fetched live from OpenAlex

The Canadian Intellectual Property Office (CIPO) is an agency of Industry Canada responsible for the administration and processing of the greater part of intellectual property (IP) in Canada. For example, during the last fiscal year, CIPO processed approximately 100,000 patents, trade-marks, copyrights and industrial designs applications. CIPO also plays a key role in supporting Canada’s innovation performance by delivering quality and timely intellectual property rights within a modern and competitive system. CIPO strives to increase Canada’s opportunities for innovation by promoting and disseminating IP rights and information to Canadian entrepreneurs, researchers and post-secondary students.In connection with the CEEA/ACEG Conference goals to enhance the competence of graduates from Canadian engineering schools through continuous improvement in engineering education and design education, CIPO has prepared a presentation to delegates on the subject of IP and the use of IP Case Studies as a means to raise the awareness of IP amongst engineering students. During this presentation, participants will leave with a better understanding of IP and will have the opportunity to exchange on the subject; as well as understand which teaching tools are available to assist engineering faculty in introducing IP in their classrooms.

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)0.000
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 score0.108
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.179
Teacher spread0.174 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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